Ansarinoorie2001/Mini-kugal
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: Ansarinoorie2001/Mini-kugal
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-04-27 22:14:52 +08:00
commit 56e1824dc6
28 changed files with 150457 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

37
.gitattributes vendored Normal file
View File

@@ -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
onnx/model.onnx_data filter=lfs diff=lfs merge=lfs -text
onnx/model_fp16.onnx_data filter=lfs diff=lfs merge=lfs -text

336
README.md Normal file
View File

@@ -0,0 +1,336 @@
---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- safetensors
- onnx
- transformers.js
base_model:
- HuggingFaceTB/SmolLM2-1.7B
---
# SmolLM2
![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/y45hIMNREW7w_XpHYB_0q.png)
## Table of Contents
1. [Model Summary](#model-summary)
2. [Evaluation](#evaluation)
3. [Examples](#examples)
4. [Limitations](#limitations)
5. [Training](#training)
6. [License](#license)
7. [Citation](#citation)
## Model Summary
SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. More details in our paper: https://arxiv.org/abs/2502.02737v1
The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1).
You can find the SFT dataset here: https://huggingface.co/datasets/HuggingFaceTB/smoltalk.
For more details refer to: https://github.com/huggingface/smollm. You will find pre-training, post-training, evaluation and local inference code.
### How to use
#### Transformers
```bash
pip install transformers
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
messages = [{"role": "user", "content": "What is the capital of France."}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))
```
#### Chat in TRL
You can also use the TRL CLI to chat with the model from the terminal:
```bash
pip install trl
trl chat --model_name_or_path HuggingFaceTB/SmolLM2-1.7B-Instruct --device cpu
```
#### Transformers.js
```bash
npm i @huggingface/transformers
```
```js
import { pipeline } from "@huggingface/transformers";
// Create a text generation pipeline
const generator = await pipeline(
"text-generation",
"HuggingFaceTB/SmolLM2-1.7B-Instruct",
);
// Define the list of messages
const messages = [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Tell me a joke." },
];
// Generate a response
const output = await generator(messages, { max_new_tokens: 128 });
console.log(output[0].generated_text.at(-1).content);
// "Why don't scientists trust atoms?\n\nBecause they make up everything!"
```
## Evaluation
In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them.
## Base Pre-Trained Model
| Metric | SmolLM2-1.7B | Llama-1B | Qwen2.5-1.5B | SmolLM1-1.7B |
|------------------|--------------|-------------|---------------|--------------|
| HellaSwag | **68.7** | 61.2 | 66.4 | 62.9 |
| ARC (Average) | **60.5** | 49.2 | 58.5 | 59.9 |
| PIQA | **77.6** | 74.8 | 76.1 | 76.0 |
| MMLU-Pro (MCF) | **19.4** | 11.7 | 13.7 | 10.8 |
| CommonsenseQA | **43.6** | 41.2 | 34.1 | 38.0 |
| TriviaQA | **36.7** | 28.1 | 20.9 | 22.5 |
| Winogrande | **59.4** | 57.8 | 59.3 | 54.7 |
| OpenBookQA | 42.2 | 38.4 | 40.0 | **42.4** |
| GSM8K (5-shot) | 31.0 | 7.2 | **61.3** | 5.5 |
## Instruction Model
| Metric | SmolLM2-1.7B-Instruct | Llama-1B-Instruct | Qwen2.5-1.5B-Instruct | SmolLM1-1.7B-Instruct |
|:-----------------------------|:---------------------:|:-----------------:|:----------------------:|:----------------------:|
| IFEval (Average prompt/inst) | **56.7** | 53.5 | 47.4 | 23.1 |
| MT-Bench | 6.13 | 5.48 | **6.52** | 4.33 |
| OpenRewrite-Eval (micro_avg RougeL) | 44.9 | 39.2 | **46.9** | NaN |
| HellaSwag | **66.1** | 56.1 | 60.9 | 55.5 |
| ARC (Average) | **51.7** | 41.6 | 46.2 | 43.7 |
| PIQA | **74.4** | 72.3 | 73.2 | 71.6 |
| MMLU-Pro (MCF) | 19.3 | 12.7 | **24.2** | 11.7 |
| BBH (3-shot) | 32.2 | 27.6 | **35.3** | 25.7 |
| GSM8K (5-shot) | **48.2** | 26.8 | 42.8 | 4.62 |
## Examples
Below are some system and instruct prompts that work well for special tasks
### Text rewriting
```python
system_prompt_rewrite = "You are an AI writing assistant. Your task is to rewrite the user's email to make it more professional and approachable while maintaining its main points and key message. Do not return any text other than the rewritten message."
user_prompt_rewrite = "Rewrite the message below to make it more friendly and approachable while maintaining its main points and key message. Do not add any new information or return any text other than the rewritten message\nThe message:"
messages = [{"role": "system", "content": system_prompt_rewrite}, {"role": "user", "content":f"{user_prompt_rewrite} The CI is failing after your last commit!"}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))
```
```
Hey there! I noticed that the CI isn't passing after your latest commit. Could you take a look and let me know what's going on? Thanks so much for your help!
```
### Summarization
```python
system_prompt_summarize = "Provide a concise, objective summary of the input text in up to three sentences, focusing on key actions and intentions without using second or third person pronouns."
messages = [{"role": "system", "content": system_prompt_summarize}, {"role": "user", "content": INSERT_LONG_EMAIL}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))
```
### Function calling
SmolLM2-1.7B-Instruct can handle function calling, it scores 27% on the [BFCL Leaderboard](https://gorilla.cs.berkeley.edu/blogs/8_berkeley_function_calling_leaderboard.html). Here's how you can leverage it:
```python
import json
import re
from typing import Optional
from jinja2 import Template
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.utils import get_json_schema
system_prompt = Template("""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.
You have access to the following tools:
<tools>{{ tools }}</tools>
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 '[]'.
<tool_call>[
{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},
... (more tool calls as required)
]</tool_call>""")
def prepare_messages(
query: str,
tools: Optional[dict[str, any]] = None,
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.
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 history:
messages = history.copy()
messages.append({"role": "user", "content": query})
else:
messages = [
{"role": "system", "content": system_prompt.render(tools=json.dumps(tools))},
{"role": "user", "content": 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"<tool_call>(.*?)</tool_call>"
matches = re.findall(pattern, text, re.DOTALL)
if matches:
return json.loads(matches[0])
return text
model_name_smollm = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name_smollm, device_map="auto", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_smollm)
from datetime import datetime
import random
def get_current_time() -> str:
"""Returns the current time in 24-hour format.
Returns:
str: Current time in HH:MM:SS format.
"""
return datetime.now().strftime("%H:%M:%S")
def get_random_number_between(min: int, max: int) -> int:
"""
Gets a random number between min and max.
Args:
min: The minimum number.
max: The maximum number.
Returns:
A random number between min and max.
"""
return random.randint(min, max)
tools = [get_json_schema(get_random_number_between), get_json_schema(get_current_time)]
toolbox = {"get_random_number_between": get_random_number_between, "get_current_time": get_current_time}
query = "Give me a number between 1 and 300"
messages = prepare_messages(query, tools=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)
tool_calls = parse_response(result)
# [{'name': 'get_random_number_between', 'arguments': {'min': 1, 'max': 300}}
# Get tool responses
tool_responses = [toolbox.get(tc["name"])(*tc["arguments"].values()) for tc in tool_calls]
# [63]
# For the second turn, rebuild the history of messages:
history = messages.copy()
# Add the "parsed response"
history.append({"role": "assistant", "content": result})
query = "Can you give me the hour?"
history.append({"role": "user", "content": query})
inputs = tokenizer.apply_chat_template(history, 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)
tool_calls = parse_response(result)
tool_responses = [toolbox.get(tc["name"])(*tc["arguments"].values()) for tc in tool_calls]
# ['07:57:25']
```
More details such as parallel function calls and tools not available can be found [here](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct/blob/main/instructions_function_calling.md)
## Limitations
SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.
## Training
### Model
- **Architecture:** Transformer decoder
- **Pretraining tokens:** 11T
- **Precision:** bfloat16
### Hardware
- **GPUs:** 256 H100
### Software
- **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/main)
- **Alignment Handbook** [alignment-handbook](https://github.com/huggingface/alignment-handbook/)
## License
[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Citation
```bash
@misc{allal2025smollm2smolgoesbig,
title={SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model},
author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Guilherme Penedo and Lewis Tunstall and Andrés Marafioti and Hynek Kydlíček and Agustín Piqueres Lajarín and Vaibhav Srivastav and Joshua Lochner and Caleb Fahlgren and Xuan-Son Nguyen and Clémentine Fourrier and Ben Burtenshaw and Hugo Larcher and Haojun Zhao and Cyril Zakka and Mathieu Morlon and Colin Raffel and Leandro von Werra and Thomas Wolf},
year={2025},
eprint={2502.02737},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.02737},
}
```

22
all_results.json Normal file
View File

@@ -0,0 +1,22 @@
{
"epoch": 2.996074326092646,
"eval_logits/chosen": -0.34099623560905457,
"eval_logits/rejected": -0.3685227334499359,
"eval_logps/chosen": -310.2510070800781,
"eval_logps/rejected": -275.43145751953125,
"eval_loss": 0.587827205657959,
"eval_rewards/accuracies": 0.6746031641960144,
"eval_rewards/chosen": 0.01673175022006035,
"eval_rewards/margins": 0.5906793475151062,
"eval_rewards/rejected": -0.573947548866272,
"eval_runtime": 18.8462,
"eval_samples": 2000,
"eval_samples_per_second": 106.122,
"eval_steps_per_second": 3.343,
"total_flos": 0.0,
"train_loss": 0.5334697115221363,
"train_runtime": 7355.3343,
"train_samples": 61134,
"train_samples_per_second": 24.935,
"train_steps_per_second": 0.195
}

40
config.json Normal file
View File

@@ -0,0 +1,40 @@
{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 8192,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 24,
"num_key_value_heads": 32,
"pad_token_id": 2,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 130000,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.42.3",
"transformers.js_config": {
"dtype": "q4",
"kv_cache_dtype": {
"q4f16": "float16",
"fp16": "float16"
},
"use_external_data_format": {
"model.onnx": true,
"model_fp16.onnx": true
}
},
"use_cache": true,
"vocab_size": 49152
}

16
eval_results.json Normal file
View File

@@ -0,0 +1,16 @@
{
"epoch": 2.996074326092646,
"eval_logits/chosen": -0.34099623560905457,
"eval_logits/rejected": -0.3685227334499359,
"eval_logps/chosen": -310.2510070800781,
"eval_logps/rejected": -275.43145751953125,
"eval_loss": 0.587827205657959,
"eval_rewards/accuracies": 0.6746031641960144,
"eval_rewards/chosen": 0.01673175022006035,
"eval_rewards/margins": 0.5906793475151062,
"eval_rewards/rejected": -0.573947548866272,
"eval_runtime": 18.8462,
"eval_samples": 2000,
"eval_samples_per_second": 106.122,
"eval_steps_per_second": 3.343
}

7
generation_config.json Normal file
View File

@@ -0,0 +1,7 @@
{
"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 2,
"transformers_version": "4.42.3"
}

183
instructions_function_calling.md Normal file
View File

@@ -0,0 +1,183 @@
## Quick start
Instructions for funtion calling:
```python
import json
import re
from typing import Optional
from jinja2 import Template
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.utils import get_json_schema
system_prompt = Template("""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.
You have access to the following tools:
<tools>{{ tools }}</tools>
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 '[]'.
<tool_call>[
{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},
... (more tool calls as required)
]</tool_call>""")
def prepare_messages(
query: str,
tools: Optional[dict[str, any]] = None,
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.
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 history:
messages = history.copy()
messages.append({"role": "user", "content": query})
else:
messages = [
{"role": "system", "content": system_prompt.render(tools=json.dumps(tools))},
{"role": "user", "content": 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"<tool_call>(.*?)</tool_call>"
matches = re.findall(pattern, text, re.DOTALL)
if matches:
return json.loads(matches[0])
return text
model_name_smollm = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name_smollm, device_map="auto", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_smollm)
from datetime import datetime
import random
def get_current_time() -> str:
"""Returns the current time in 24-hour format.
Returns:
str: Current time in HH:MM:SS format.
"""
return datetime.now().strftime("%H:%M:%S")
def get_random_number_between(min: int, max: int) -> int:
"""
Gets a random number between min and max.
Args:
min: The minimum number.
max: The maximum number.
Returns:
A random number between min and max.
"""
return random.randint(min, max)
tools = [get_json_schema(get_random_number_between), get_json_schema(get_current_time)]
toolbox = {"get_random_number_between": get_random_number_between, "get_current_time": get_current_time}
query = "Give me a number between 1 and 300"
messages = prepare_messages(query, tools=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)
tool_calls = parse_response(result)
# [{'name': 'get_random_number_between', 'arguments': {'min': 1, 'max': 300}}
# Get tool responses
tool_responses = [toolbox.get(tc["name"])(*tc["arguments"].values()) for tc in tool_calls]
# [63]
# For the second turn, rebuild the history of messages:
history = messages.copy()
# Add the "parsed response"
history.append({"role": "assistant", "content": result})
query = "Can you give me the hour?"
history.append({"role": "user", "content": query})
inputs = tokenizer.apply_chat_template(history, 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)
tool_calls = parse_response(result)
tool_responses = [toolbox.get(tc["name"])(*tc["arguments"].values()) for tc in tool_calls]
# ['07:57:25']
```
#### Parallel function calls
Multiple calls required by the same query.
```python
query = "Can you give me the hour and a random number between 1 and 50?"
messages = prepare_messages(query, tools=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)
tool_calls = parse_response(result)
tool_responses = [toolbox.get(tc["name"])(*tc["arguments"].values()) for tc in tool_calls]
# ['09:24:52', 50]
query = "Can you give me a random number between 1 and 10, other between 200 and 210 and another one between 55 and 60?"
messages = prepare_messages(query, tools=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)
tool_calls = parse_response(result)
tool_responses = [toolbox.get(tc["name"])(*tc["arguments"].values()) for tc in tool_calls]
# [7, 202, 60]
```
#### Tools not available
```python
query = "Can you open a new page with youtube?"
messages = prepare_messages(query, tools=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)
tool_calls = parse_response(result)
# []
# The response will be something similar to the following:
# "The query cannot be answered with the provided tools. Please make sure the tools are correctly installed and imported. If the tools are not installed, install them using pip: 'pip install -r tools.txt'. If the tools are already installed, ensure they are correctly configured. If the tools are not correctly configured, please contact the support team. The output MUST strictly adhere to the following format, and NO other text MUST be included.\n\n<tool_call>[]</tool_call>"
```

48901
merges.txt Normal file
View File

File diff suppressed because it is too large Load Diff

3
model.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f55217be716b6a997b97b9d8d7eb6fad02e00858f5010ec24f64603c3a98a0e8
size 3422777952

3
onnx/model.onnx Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c538daa78f811830dc9028aa228a63a218147ab478c0c65ef6e2d8cab532380a
size 165580

3
onnx/model.onnx_data Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:023686a59a534e45af70bc5f99ae70e592481701680591f9844fc140a3db220a
size 6847602688

3
onnx/model_bnb4.onnx Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ac7400ca170e6644d95d26394846d2cce8083298e8519b1df7ac1eaa0ab2b5b6
size 1311307655

3
onnx/model_fp16.onnx Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:3d891d77661f6339f727b3188f1c21b1429a1e4e55c40d1f60f85048b00a4348
size 1326807956

3
onnx/model_fp16.onnx_data Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:5f48c05c14ed97738f8dc5854c20c229ddc8661f43fa914085843901a4ba8740
size 2097152000

3
onnx/model_int8.onnx Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:db5cb9057f4e7014f00c38fd9764f0103e60bb4d145ad600b7144625f0d56930
size 1714119778

3
onnx/model_q4.onnx Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:467b7b8f62d99f184d3628d24b8d65c151e331695f6e9ea997616c4e279e9a51
size 1411969607

3
onnx/model_q4f16.onnx Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:d94946187fb5f27579f3db4ba21fb7f7466c7cbd18956bd420d3981f75282f9c
size 1108730338

3
onnx/model_quantized.onnx Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b1110061268f161512c234c4d43cd9dbe61eeb1454e9b94b652edf4de55ae51c
size 1714119846

3
onnx/model_uint8.onnx Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b1110061268f161512c234c4d43cd9dbe61eeb1454e9b94b652edf4de55ae51c
size 1714119846

3
runs/Oct31_06-24-59_ip-26-0-174-36/events.out.tfevents.1730356365.ip-26-0-174-36.3169719.0 Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e6bfce1916438dd2e6553aa0a62d418087b3ae04f8af75e714ad1f01b7663db6
size 114828

3
runs/Oct31_06-24-59_ip-26-0-174-36/events.out.tfevents.1730363825.ip-26-0-174-36.3169719.1 Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b3d7723fd0715ce6dcbccf7bb2097f59490b0ac670f798f5378ef5abb7d1301d
size 828

34
special_tokens_map.json Normal file
View File

@@ -0,0 +1,34 @@
{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>"
],
"bos_token": {
"content": "<|im_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

98249
tokenizer.json Normal file
View File

File diff suppressed because it is too large Load Diff

154
tokenizer_config.json Normal file
View File

@@ -0,0 +1,154 @@
{
"add_prefix_space": false,
"added_tokens_decoder": {
"0": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "<|im_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"3": {
"content": "<repo_name>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"4": {
"content": "<reponame>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"5": {
"content": "<file_sep>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"6": {
"content": "<filename>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"7": {
"content": "<gh_stars>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"8": {
"content": "<issue_start>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"9": {
"content": "<issue_comment>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"10": {
"content": "<issue_closed>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"11": {
"content": "<jupyter_start>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"12": {
"content": "<jupyter_text>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"13": {
"content": "<jupyter_code>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"14": {
"content": "<jupyter_output>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"15": {
"content": "<jupyter_script>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"16": {
"content": "<empty_output>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>"
],
"bos_token": "<|im_start|>",
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"model_max_length": 8192,
"pad_token": "<|im_end|>",
"tokenizer_class": "GPT2Tokenizer",
"unk_token": "<|endoftext|>",
"vocab_size": 49152
}

9
train_results.json Normal file
View File

@@ -0,0 +1,9 @@
{
"epoch": 2.996074326092646,
"total_flos": 0.0,
"train_loss": 0.5334697115221363,
"train_runtime": 7355.3343,
"train_samples": 61134,
"train_samples_per_second": 24.935,
"train_steps_per_second": 0.195
}

2426
trainer_state.json Normal file
View File

File diff suppressed because it is too large Load Diff

3
training_args.bin Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:7649586c424c337f6c403fdb617ac9d954daf9a7192f3afe5b6318f37e9bb19e
size 6520

1
vocab.json Normal file
View File

File diff suppressed because one or more lines are too long