commit 89ec761a2cd6637a711d64b11891ce2b6a508e3f Author: ModelHub XC Date: Wed May 6 08:42:29 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: argilla/Llama-3.2-1B-Instruct-APIGen-FC-v0.1 Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..602d20f --- /dev/null +++ b/.gitattributes @@ -0,0 +1,37 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +model.safetensors filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..027f680 --- /dev/null +++ b/README.md @@ -0,0 +1,392 @@ +--- +base_model: meta-llama/Llama-3.2-1B-Instruct +datasets: +- argilla-warehouse/apigen-synth-trl +library_name: transformers +model_name: Llama-3.2-1B-Instruct-APIGen-FC-v0.1 +tags: +- generated_from_trainer +- trl +- sft +licence: license +license: apache-2.0 +language: +- en +--- + +# Model Card for Llama-3.2-1B-Instruct-APIGen-FC-v0.1 + +This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on +[argilla-warehouse/apigen-synth-trl](https://huggingface.co/datasets/argilla-warehouse/apigen-synth-trl) dataset, a version of +[argilla/Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla-warehouse/Synth-APIGen-v0.1) ready to do SFT on top of it. +It has been trained using [TRL](https://github.com/huggingface/trl). + +## Quick start + +This is a Fine tuned version of `Llama-3.2-1B-Instruct` model specific for Function Calling, to showcase how to fine tune a model on top of a dataset +like [argilla/Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1). + +### Helper functions for the prompt and output parsing + +
Click to see helper functions + +````python +from typing import Optional +import re +import json + +from jinja2 import Template + +SYSTEM_PROMPT = """ +You are an expert in composing functions. You are given a question and a set of possible functions. +Based on the question, you will need to make one or more function/tool calls to achieve the purpose. +If none of the functions can be used, point it out and refuse to answer. +If the given question lacks the parameters required by the function, also point it out. + +The output MUST strictly adhere to the following format, and NO other text MUST be included. +The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make the tool calls an empty list '[]'. +``` +[ +{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}}, +... (more tool calls as required) +] +``` +""".strip() + +prompt = Template(""" +You have access to the following tools: +{{ tools }} + +Please answer the following query: +{{ query }} +""".lstrip()) + +def prepare_messages( + query: str, + tools: Optional[dict[str, any]] = None, + conversation_history: Optional[list[dict[str, str]]] = None +) -> list[dict[str, str]]: + """Prepare the system and user messages for the given query and tools. + + Args: + query: The query to be answered. + tools: The tools available to the user. Defaults to None, in which case if a + list without content will be passed to the model. + conversation_history: Exchange of messages, including the system_prompt from + the first query. Defaults to None, the first message in a conversation. + """ + if tools is None: + tools = [] + + if conversation_history: + messages = conversation_history.copy() + messages.append({"role": "user", "content": query}) + else: + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": prompt.render(tools=json.dumps(tools), query=query)} + ] + + return messages + + +def parse_response(text: str) -> str | dict[str, any]: + """Parses a response from the model, returning either the + parsed list with the tool calls parsed, or the + model thought or response if couldn't generate one. + + Args: + text: Response from the model. + """ + pattern = r"(.*?)" + matches = re.findall(pattern, text, re.DOTALL) + if matches: + return json.loads(matches[0]) + return text + +```` + +
+ +### Examples + +The following examples show how to use the model with transformers, for different types of queries and depending on the availability of tools. + + +Example of *simple* function call: + +````python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = "argilla-warehouse/Llama-3.2-1B-Instruct-APIGen-FC-v0.1" +model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True) +tokenizer = AutoTokenizer.from_pretrained(model_name) + + +get_weather_api = { + "name": "get_weather", + "description": "Get the current weather for a location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, New York" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The unit of temperature to return" + } + }, + "required": ["location"] + } +} + +search_api = { + "name": "search", + "description": "Search for information on the internet", + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "The search query, e.g. 'latest news on AI'" + } + }, + "required": ["query"] + } +} + +available_tools = [get_weather_api, search_api] + +query = "What's the weather like in New York in fahrenheit?" + +messages = prepare_messages(query, tools=available_tools) + +inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) + +outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) +result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=False) + +response = parse_response(result) +# [{'name': 'get_weather', 'arguments': {'location': 'New York', 'unit': 'fahrenheit'}}] +```` + +#### `Parallel` function call + +
Click here: + +```python +available_tools = [{"name": "spotify.play", "description": "Play specific tracks from a given artist for a specific time duration.", "parameters": {"type": "dict", "properties": {"artist": {"type": "string", "description": "The artist whose songs you want to play."}, "duration": {"type": "integer", "description": "The duration for which the songs should be played, in minutes."}}, "required": ["artist", "duration"]}}] +query = "Play songs from the artists Taylor Swift and Maroon 5, with a play time of 20 minutes and 15 minutes respectively, on Spotify." + +messages = prepare_messages(query, tools=available_tools) + +inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) +outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) +result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=False) + +response = parse_response(result) +# [{'name': 'spotify.play', 'arguments': {'artist': 'Taylor Swift', 'duration': 20}}, {'name': 'spotify.play', 'arguments': {'artist': 'Maroon 5', 'duration': 15}}] +``` + +
+ +#### `Multiple` function call + + +
Click here: + +```python +available_tools = [{"name": "country_info.largest_city", "description": "Fetch the largest city of a specified country.", "parameters": {"type": "dict", "properties": {"country": {"type": "string", "description": "Name of the country."}}, "required": ["country"]}}, {"name": "country_info.capital", "description": "Fetch the capital city of a specified country.", "parameters": {"type": "dict", "properties": {"country": {"type": "string", "description": "Name of the country."}}, "required": ["country"]}}, {"name": "country_info.population", "description": "Fetch the current population of a specified country.", "parameters": {"type": "dict", "properties": {"country": {"type": "string", "description": "Name of the country."}}, "required": ["country"]}}] +query = "What is the capital of Brazil?" + +messages = prepare_messages(query, tools=available_tools) + +inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) +outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) +result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=False) + +response = parse_response(result) +# [{'name': 'country_info.capital', 'arguments': {'country': 'Brazil'}}] +``` + +
+ +#### `Parallel multiple` function call + + +
Click here: + +```python +available_tools = [{"name": "math_toolkit.sum_of_multiples", "description": "Find the sum of all multiples of specified numbers within a specified range.", "parameters": {"type": "dict", "properties": {"lower_limit": {"type": "integer", "description": "The start of the range (inclusive)."}, "upper_limit": {"type": "integer", "description": "The end of the range (inclusive)."}, "multiples": {"type": "array", "items": {"type": "integer"}, "description": "The numbers to find multiples of."}}, "required": ["lower_limit", "upper_limit", "multiples"]}}, {"name": "math_toolkit.product_of_primes", "description": "Find the product of the first n prime numbers.", "parameters": {"type": "dict", "properties": {"count": {"type": "integer", "description": "The number of prime numbers to multiply together."}}, "required": ["count"]}}] +query = "Find the sum of all the multiples of 3 and 5 between 1 and 1000. Also find the product of the first five prime numbers." + +messages = prepare_messages(query, tools=available_tools) + +inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) +outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) +result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=False) + +response = parse_response(result) +# [{'name': 'math_toolkit.sum_of_multiples', 'arguments': {'lower_limit': 1, 'upper_limit': 1000, 'multiples': [3, 5]}}, {'name': 'math_toolkit.product_of_primes', 'arguments': {'count': 5}}] +``` + +
+ +#### `Multi-turn` function call + + +
Click here: + +```python + +get_weather_api = { + "name": "get_weather", + "description": "Get the current weather for a location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, New York" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The unit of temperature to return" + } + }, + "required": ["location"] + } +} + +available_tools = [get_weather_api] + +query = "What's the weather like in Madrid in celsius?" + +messages = prepare_messages(query, tools=available_tools) + +inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) + +outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) +result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=False) + +response = parse_response(result) + +# 2nd turn +conversation_history = messages.copy() +conversation_history.append({"role": "assistant", "content": json.dumps(response)}) + +new_query = "And in Edinburgh in celsius?" + +new_messages = prepare_messages(new_query, tools=available_tools, conversation_history=conversation_history) + +inputs = tokenizer.apply_chat_template(new_messages, add_generation_prompt=True, return_tensors="pt").to(model.device) + +outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) +result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=False) + +response = parse_response(result) +# [{'name': 'get_weather', 'arguments': {'location': 'Edinburgh', 'unit': 'celsius'}}] +``` + +
+ +#### `Irrelevance` function call (examples when some data is missing) + + +
Click here: + +Example response with no tools available + +```python +available_tools = [] + +query = "What's the weather like in New York in fahrenheit?" + +messages = prepare_messages(query, tools=available_tools) + +inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) + +outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) +result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True) +response = parse_response(result) +# 'The query cannot be answered, no tools were provided.' +``` + +Example when a wrong tool is informed: + +```python +cut_number = { + 'type': 'function', + 'function': { + 'name': 'cut_number', + 'description': 'Returns the value `number` if it is greater than or equal to `threshold`, otherwise returns the value `threshold`.', + 'parameters': { + 'type': 'object', + 'properties': {'number': {'type': 'number', 'description': 'The number to compare.'}}, + 'required': ['number'] + } + } +} + +available_tools = [cut_number] + +query = "What's the weather like in New York in fahrenheit?" + +messages = prepare_messages(query, tools=available_tools) +inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) + +outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) +result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True) +response = parse_response(result) +# "The query cannot be answered with the provided tools. The query lacks the parameters required by the function. Please provide the parameters, and I'll be happy to assist." +``` + +
+ +## Training procedure + +[Visualize in Weights & Biases](https://wandb.ai/plaguss/huggingface/runs/dw9q43g4) + +This model was trained with SFT. You can take a look at [sft.slurm](https://huggingface.co/argilla/Llama-3.2-1B-Instruct-APIGen-FC-v0.1/blob/main/sft.slurm) to see the +training script, if you don't have access to a slurm cluster, it can be run jsut using the `accelerate` command. It took 13 minutes in a node with 8xH100. + +To install the requirements, the following commands can be used: + +```bash +uv venv .venv --python 3.11 +source .venv/bin/activate +git clone https://github.com/huggingface/trl.git +uv pip install . +uv pip install wandb +uv pip install deepspeed +``` + +And login to your WandB and Hugging Face accounts to push both logs and the final model. + +### Framework versions + +- TRL: 0.12.0.dev0 +- Transformers: 4.45.1 +- Pytorch: 2.4.1 +- Datasets: 3.0.1 +- Tokenizers: 0.20.0 + +## Citations + +Cite TRL as: + +```bibtex +@misc{vonwerra2022trl, + title = {{TRL: Transformer Reinforcement Learning}}, + author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, + year = 2020, + journal = {GitHub repository}, + publisher = {GitHub}, + howpublished = {\url{https://github.com/huggingface/trl}} +} +``` \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..2fe47fa --- /dev/null +++ b/config.json @@ -0,0 +1,40 @@ +{ + "_name_or_path": "meta-llama/Llama-3.2-1B-Instruct", + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "head_dim": 64, + "hidden_act": "silu", + "hidden_size": 2048, + "initializer_range": 0.02, + "intermediate_size": 8192, + "max_position_embeddings": 131072, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 16, + "num_key_value_heads": 8, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "factor": 32.0, + "high_freq_factor": 4.0, + "low_freq_factor": 1.0, + "original_max_position_embeddings": 8192, + "rope_type": "llama3" + }, + "rope_theta": 500000.0, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "transformers_version": "4.45.1", + "use_cache": false, + "vocab_size": 128256 +} diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..bbeeda1 --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"framework": "pytorch", "task": "text-generation", "allow_remote": true} \ No newline at end of file diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..c48a399 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "bos_token_id": 128000, + "do_sample": true, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "temperature": 0.6, + "top_p": 0.9, + "transformers_version": "4.45.1" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..ff6fde5 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:359c06fd3d95a9403b172f54e04f81daa3f3295d40d0e9f1c1f39f2a7ad4e33a +size 2471645608 diff --git a/sft.slurm b/sft.slurm new file mode 100644 index 0000000..675a41a --- /dev/null +++ b/sft.slurm @@ -0,0 +1,39 @@ +#!/bin/bash +#SBATCH --job-name=apigen-fine-tune +#SBATCH --partition=hopper-prod +#SBATCH --qos=normal +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --gpus-per-node=8 +#SBATCH --output=./logs/%x-%j.out +#SBATCH --err=./logs/%x-%j.err +#SBATCH --time=02-00:00:00 + +set -ex + +module load cuda/12.1 + +source .venv/bin/activate + +srun --nodes=1 --ntasks=1 --export=ALL,ACCELERATE_LOG_LEVEL=info accelerate launch --config_file examples/accelerate_configs/deepspeed_zero3.yaml examples/scripts/sft.py \ + --run_name=Llama-3.2-1B-Instruct-APIGen-FC-v0.1 \ + --model_name_or_path="meta-llama/Llama-3.2-1B-Instruct" \ + --dataset_name="plaguss/apigen-synth-trl" \ + --report_to="wandb" \ + --learning_rate=5.0e-06 \ + --lr_scheduler_type="cosine" \ + --per_device_train_batch_size=6 \ + --per_device_eval_batch_size=6 \ + --do_eval \ + --eval_strategy="steps" \ + --gradient_accumulation_steps=2 \ + --output_dir="data/Llama-3.2-1B-Instruct-APIGen-FC-v0.1" \ + --logging_steps=5 \ + --eval_steps=50 \ + --num_train_epochs=2 \ + --max_steps=-1 \ + --warmup_steps=50 \ + --max_seq_length=2048 \ + --push_to_hub \ + --gradient_checkpointing \ + --bf16 diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..b43be96 --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,17 @@ +{ + "bos_token": { + "content": "<|begin_of_text|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "<|eot_id|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "<|eot_id|>" +} diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..6ac83ff --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:384a7e7c676f7be2e5d2e8449c508be9b00e5b18c5b3c39ebc626e96b3f4b988 +size 17210019 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+ "128253": { + "content": "<|reserved_special_token_245|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128254": { + "content": "<|reserved_special_token_246|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128255": { + "content": "<|reserved_special_token_247|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "bos_token": "<|begin_of_text|>", + "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n", + "clean_up_tokenization_spaces": true, + "eos_token": "<|eot_id|>", + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 131072, + "pad_token": "<|eot_id|>", + "tokenizer_class": "PreTrainedTokenizerFast" +} diff --git a/training_args.bin b/training_args.bin new file mode 100644 index 0000000..646fcba --- /dev/null +++ b/training_args.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b6e23403530c5021a1e43bded53a75a81326a56c1c4dcc8bb2d1087321c17d16 +size 6904