crossfile_context_retrievalwref dict | prompt stringlengths 252 32.6k | right_context stringlengths 0 81.2k | metadata dict | crossfile_context_retrieval dict | groundtruth stringlengths 5 208 |
|---|---|---|---|---|---|
{
"list": [
{
"filename": "alt_generator.py",
"retrieved_chunk": " if self.remaining_tokens == 0:\n self.sequence_str += self.held_text\n return self.held_text, True\n self.remaining_tokens -= 1\n # Decode the current tail end of the sequence\n old_tai... | import asyncio
import websockets
import json
from sentencepiece import SentencePieceProcessor
from model import ExLlama, ExLlamaCache, ExLlamaConfig
from lora import ExLlamaLora
from tokenizer import ExLlamaTokenizer
from generator import ExLlamaGenerator
import argparse
import torch
import sys
import os
import glob
i... |
next_token = generator.gen_single_token()
# End on stop token
if next_token in stop_tokens:
return held_text, True, full_prompt + built_response, utilized_prompt + built_response, built_response
# Get new text
new_tail = tokenizer.decode(generator.sequence_actual[:, -(max_stop_string + ... | {
"context_start_lineno": 0,
"file": "example_ws.py",
"groundtruth_start_lineno": 103,
"repository": "turboderp-exllama-a544085",
"right_context_start_lineno": 104,
"task_id": "project_cc_python/62"
} | {
"list": [
{
"filename": "alt_generator.py",
"retrieved_chunk": " self.sequence_str += self.held_text\n return self.held_text, True\n # Decode the tail end of the sequence with the added token to get (actual) characters added\n new_tail = self.tokenizer.decode(self... | sequence_actual[:, -max_stop_string:])[0] |
{
"list": [
{
"filename": "generator.py",
"retrieved_chunk": " logits = self.model.forward(self.sequence[:, -1:], self.cache, lora = self.lora, input_mask = mask)\n self.apply_rep_penalty(logits)\n logits[:, :, self.tokenizer.bos_token_id] = -10000.0\n if co... | from model import ExLlama, ExLlamaCache, ExLlamaConfig
from tokenizer import ExLlamaTokenizer
from generator import ExLlamaGenerator
import torch
import torch.nn.functional as F
import os, glob
import cuda_ext
# Directory containing model, tokenizer, generator
model_directory = "/mnt/str/models/_test_models/TheBloke... |
output = tokenizer.decode(generator.sequence[0])
return output
for i in range(10):
alpha = i / 5.0 - 0.4
print()
print(f"--------------------------------------")
print(f"alpha = {alpha:.1f}")
print(f"--------------------------------------")
output = generate_cfg(prompts, alpha, 200)
... | {
"context_start_lineno": 0,
"file": "example_cfg.py",
"groundtruth_start_lineno": 78,
"repository": "turboderp-exllama-a544085",
"right_context_start_lineno": 79,
"task_id": "project_cc_python/74"
} | {
"list": [
{
"filename": "generator.py",
"retrieved_chunk": " self.settings.min_p + 0.01 if constraints is not None else 0.0,\n self.settings.typical)\n else:\n # bos = torch.Tensor([[self.tokenizer.... | gen_accept_token(batch_token) |
{
"list": [
{
"filename": "webui/app.py",
"retrieved_chunk": "def api_delete_session():\n global session\n data = request.get_json()\n session.api_delete_session(data)\n return json.dumps({\"result\": \"ok\"}) + \"\\n\"\n# Set fixed prompt settings\n@app.route(\"/api/set_fixed_prompt\", me... | from model import ExLlama, ExLlamaCache, ExLlamaConfig
from flask import Flask, request
from tokenizer import ExLlamaTokenizer
from generator import ExLlamaGenerator
import os, glob
# Directory containing config.json, tokenizer.model and safetensors file for the model
model_directory = "/mnt/str/models/llama-7b-4bit/"... |
generator.settings.token_repetition_penalty_sustain = config.max_seq_len
generator.settings.temperature = 0.7
generator.settings.top_p = 0.1
generator.settings.top_k = 40
generator.settings.typical = 0.0 # Disabled
outputs = generator.generate_simple(prompt, max_new_tokens = 200)
return... | {
"context_start_lineno": 0,
"file": "example_flask.py",
"groundtruth_start_lineno": 36,
"repository": "turboderp-exllama-a544085",
"right_context_start_lineno": 37,
"task_id": "project_cc_python/76"
} | {
"list": [
{
"filename": "webui/app.py",
"retrieved_chunk": "def home():\n return render_template(\"index.html\")\n# Get existing sessions\n@app.route(\"/api/populate\")\ndef api_populate():\n global session\n return session.api_populate()\n# Edit block\n@app.route(\"/api/edit_block\", metho... | settings.token_repetition_penalty_max = 1.176 |
{
"list": [
{
"filename": "alt_generator.py",
"retrieved_chunk": " # stop_conditions: List of strings or integer token IDs that will end the sequence\n # settings: ExLlamaAltGeneratorSettings\n # encode_special_characters: Set to true to tokenize \"</s>\" etc.\n def begin_stream(self, prom... | import asyncio
import websockets
import json
from sentencepiece import SentencePieceProcessor
from model import ExLlama, ExLlamaCache, ExLlamaConfig
from lora import ExLlamaLora
from tokenizer import ExLlamaTokenizer
from generator import ExLlamaGenerator
import argparse
import torch
import sys
import os
import glob
i... |
built_response = ""
remaining_tokens = max_new_tokens
# Settings
stop_strings = []
stop_tokens = []
for t in stop_conditions:
if isinstance(t, int): stop_tokens += [t]
if isinstance(t, str): stop_strings += [t]
held_text = ""
max_stop_string = 2
for ss in stop_s... | {
"context_start_lineno": 0,
"file": "example_ws.py",
"groundtruth_start_lineno": 65,
"repository": "turboderp-exllama-a544085",
"right_context_start_lineno": 66,
"task_id": "project_cc_python/60"
} | {
"list": [
{
"filename": "alt_generator.py",
"retrieved_chunk": " self.sequence_str = self.tokenizer.decode(applied_input_ids)[0] if applied_input_ids.shape[0] < input_ids.shape[0] else prompt\n # Settings\n self.stop_strings = []\n self.stop_tokens = []\n for t in ... | decode(prompt_ids)[0] |
{
"list": [
{
"filename": "alt_generator.py",
"retrieved_chunk": " self.sequence_str = self.tokenizer.decode(applied_input_ids)[0] if applied_input_ids.shape[0] < input_ids.shape[0] else prompt\n # Settings\n self.stop_strings = []\n self.stop_tokens = []\n for t in ... | import asyncio
import websockets
import json
from sentencepiece import SentencePieceProcessor
from model import ExLlama, ExLlamaCache, ExLlamaConfig
from lora import ExLlamaLora
from tokenizer import ExLlamaTokenizer
from generator import ExLlamaGenerator
import argparse
import torch
import sys
import os
import glob
i... |
def stream():
global model, cache, config, generator, tokenizer
global stop_strings, stop_tokens, prompt_ids, held_text, max_stop_string, remaining_tokens
global full_prompt, utilized_prompt, built_response
# Check total response length
if remaining_tokens == 0:
return held_text, True, f... | {
"context_start_lineno": 0,
"file": "example_ws.py",
"groundtruth_start_lineno": 88,
"repository": "turboderp-exllama-a544085",
"right_context_start_lineno": 89,
"task_id": "project_cc_python/61"
} | {
"list": [
{
"filename": "alt_generator.py",
"retrieved_chunk": " for ss in self.stop_strings:\n self.max_stop_tokens = max(self.max_stop_tokens, self.get_num_tokens(ss) + 2)\n self.settings = gen_settings\n # Start generation\n self.gen_begin_reuse(applied_inpu... | gen_begin_reuse(input_ids) |
{
"list": [
{
"filename": "generator.py",
"retrieved_chunk": " self.sequence = self.sequence[:, num_tokens:]\n self.gen_begin(self.sequence, mask = mask)\n def gen_num_tokens(self):\n return self.sequence_actual.shape[-1]\n # Simple generator function\n def genera... | from model import ExLlama, ExLlamaCache, ExLlamaConfig
from tokenizer import ExLlamaTokenizer
from generator import ExLlamaGenerator
import torch
import torch.nn.functional as F
import os, glob
import cuda_ext
# Directory containing model, tokenizer, generator
model_directory = "/mnt/str/models/_test_models/TheBloke... |
generator.gen_begin(ids, mask = mask)
# Sampling loop
for _ in range(max_new_tokens):
logits = model.forward(generator.sequence[:, -1:], cache, input_mask = mask)
generator.apply_rep_penalty(logits)
logits = F.log_softmax(logits, dim = -1)
logits_mixed = (1 - alpha) * lo... | {
"context_start_lineno": 0,
"file": "example_cfg.py",
"groundtruth_start_lineno": 61,
"repository": "turboderp-exllama-a544085",
"right_context_start_lineno": 62,
"task_id": "project_cc_python/67"
} | {
"list": [
{
"filename": "generator.py",
"retrieved_chunk": " eos = torch.zeros((ids.shape[0],), dtype = torch.bool)\n for i in range(max_new_tokens):\n token = self.gen_single_token(mask = mask)\n for j in range(token.shape[0]):\n if token[j, 0].ite... | encode(prompts, return_mask = True) |
{
"list": [
{
"filename": "generator.py",
"retrieved_chunk": " logits = self.model.forward(self.sequence[:, -1:], self.cache, lora = self.lora, input_mask = mask)\n self.apply_rep_penalty(logits)\n logits[:, :, self.tokenizer.bos_token_id] = -10000.0\n if co... | from model import ExLlama, ExLlamaCache, ExLlamaConfig
from tokenizer import ExLlamaTokenizer
from generator import ExLlamaGenerator
import torch
import torch.nn.functional as F
import os, glob
import cuda_ext
# Directory containing model, tokenizer, generator
model_directory = "/mnt/str/models/_test_models/TheBloke... |
return output
for i in range(10):
alpha = i / 5.0 - 0.4
print()
print(f"--------------------------------------")
print(f"alpha = {alpha:.1f}")
print(f"--------------------------------------")
output = generate_cfg(prompts, alpha, 200)
print(output[len(prompts[0]):].strip())
| {
"context_start_lineno": 0,
"file": "example_cfg.py",
"groundtruth_start_lineno": 80,
"repository": "turboderp-exllama-a544085",
"right_context_start_lineno": 81,
"task_id": "project_cc_python/75"
} | {
"list": [
{
"filename": "generator.py",
"retrieved_chunk": " self.settings.min_p + 0.01 if constraints is not None else 0.0,\n self.settings.typical)\n else:\n # bos = torch.Tensor([[self.tokenizer.... | decode(generator.sequence[0]) |
{
"list": [
{
"filename": "example_alt_generator.py",
"retrieved_chunk": " args.lora = os.path.join(args.lora_dir, \"adapter_model.bin\")\n # Model globals\n model_init.set_globals(args)\n # Instantiate model and generator\n config = model_init.make_config(args)\n model = ExLlama... | from model import ExLlama, ExLlamaCache, ExLlamaConfig
from tokenizer import ExLlamaTokenizer
import argparse, sys, os, glob
from torch import version as torch_version
from globals import set_affinity_str
def add_args(parser):
parser.add_argument("-t", "--tokenizer", type = str, help = "Tokenizer model path")
... |
if args.flash_attn:
config.use_flash_attn_2 = True
try:
config.max_input_len = int(args.flash_attn)
except ValueError:
pass
config.matmul_recons_thd = args.matmul_recons_thd
config.fused_mlp_thd = args.fused_mlp_thd
config.sdp_thd = args.sdp_thd
con... | {
"context_start_lineno": 0,
"file": "model_init.py",
"groundtruth_start_lineno": 122,
"repository": "turboderp-exllama-a544085",
"right_context_start_lineno": 123,
"task_id": "project_cc_python/80"
} | {
"list": [
{
"filename": "example_alt_generator.py",
"retrieved_chunk": " lora = None\n if args.lora:\n print(f\" -- LoRA config: {args.lora_config}\")\n print(f\" -- Loading LoRA: {args.lora}\")\n if args.lora_config is None:\n print(f\" ## Error: please specify... | calculate_rotary_embedding_base() |
{
"list": [
{
"filename": "example_basic.py",
"retrieved_chunk": "generator.settings.token_repetition_penalty_max = 1.2\ngenerator.settings.temperature = 0.95\ngenerator.settings.top_p = 0.65\ngenerator.settings.top_k = 100\ngenerator.settings.typical = 0.5\n# Produce a simple generation\nprompt = \"O... | from model import ExLlama, ExLlamaCache, ExLlamaConfig
from tokenizer import ExLlamaTokenizer
from generator import ExLlamaGenerator
import os, glob
# Directory containing model, tokenizer, generator
model_directory = "/mnt/str/models/llama-13b-4bit-128g/"
# Locate files we need within that directory
tokenizer_pat... |
for line in output:
print("---")
print(line)
| {
"context_start_lineno": 0,
"file": "example_batch.py",
"groundtruth_start_lineno": 51,
"repository": "turboderp-exllama-a544085",
"right_context_start_lineno": 52,
"task_id": "project_cc_python/56"
} | {
"list": [
{
"filename": "example_basic.py",
"retrieved_chunk": "generator.settings.token_repetition_penalty_max = 1.2\ngenerator.settings.temperature = 0.95\ngenerator.settings.top_p = 0.65\ngenerator.settings.top_k = 100\ngenerator.settings.typical = 0.5\n# Produce a simple generation\nprompt = \"O... | generate_simple(prompts, max_new_tokens = 200) |
{
"list": [
{
"filename": "example_chatbot.py",
"retrieved_chunk": "print(f\" -- Sequence length: {args.length}\")\nprint(f\" -- Temperature: {args.temperature:.2f}\")\nprint(f\" -- Top-K: {args.top_k}\")\nprint(f\" -- Top-P: {args.top_p:.2f}\")\nprint(f\" -- Min-P: {args.min_p:.2f}\")\nprint(f\" -- R... | from model import ExLlama, ExLlamaCache, ExLlamaConfig
from tokenizer import ExLlamaTokenizer
import argparse, sys, os, glob
from torch import version as torch_version
from globals import set_affinity_str
def add_args(parser):
parser.add_argument("-t", "--tokenizer", type = str, help = "Tokenizer model path")
... |
config.gpu_peer_fix = args.gpu_peer_fix
config.alpha_value = args.alpha
config.calculate_rotary_embedding_base()
if args.flash_attn:
config.use_flash_attn_2 = True
try:
config.max_input_len = int(args.flash_attn)
except ValueError:
pass
config.matmu... | {
"context_start_lineno": 0,
"file": "model_init.py",
"groundtruth_start_lineno": 119,
"repository": "turboderp-exllama-a544085",
"right_context_start_lineno": 120,
"task_id": "project_cc_python/79"
} | {
"list": [
{
"filename": "example_chatbot.py",
"retrieved_chunk": "model_init.print_options(args, print_opts)\n# Globals\nmodel_init.set_globals(args)\n# Load prompt file\nusername = args.username\nbot_name = args.botname\nif args.prompt is not None:\n with open(args.prompt, \"r\") as f:\n ... | set_auto_map(args.gpu_split) |
{
"list": [
{
"filename": "generator.py",
"retrieved_chunk": " self.sequence = self.sequence[:, num_tokens:]\n self.gen_begin(self.sequence, mask = mask)\n def gen_num_tokens(self):\n return self.sequence_actual.shape[-1]\n # Simple generator function\n def genera... | from model import ExLlama, ExLlamaCache, ExLlamaConfig
from tokenizer import ExLlamaTokenizer
from generator import ExLlamaGenerator
import torch
import torch.nn.functional as F
import os, glob
import cuda_ext
# Directory containing model, tokenizer, generator
model_directory = "/mnt/str/models/_test_models/TheBloke... |
generator.apply_rep_penalty(logits)
logits = F.log_softmax(logits, dim = -1)
logits_mixed = (1 - alpha) * logits[0] + alpha * logits[1]
sampled_token, _ = generator.sample_current(logits_mixed)
if sampled_token.item() == tokenizer.eos_token_id: break
batch_token = sam... | {
"context_start_lineno": 0,
"file": "example_cfg.py",
"groundtruth_start_lineno": 68,
"repository": "turboderp-exllama-a544085",
"right_context_start_lineno": 69,
"task_id": "project_cc_python/69"
} | {
"list": [
{
"filename": "generator.py",
"retrieved_chunk": " eos = torch.zeros((ids.shape[0],), dtype = torch.bool)\n for i in range(max_new_tokens):\n token = self.gen_single_token(mask = mask)\n for j in range(token.shape[0]):\n if token[j, 0].ite... | forward(generator.sequence[:, -1:], cache, input_mask = mask) |
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