From cb17c72df9a0b48dbc56b2988899072801f8676f Mon Sep 17 00:00:00 2001 From: ai-modelscope Date: Thu, 17 Oct 2024 21:43:47 +0800 Subject: [PATCH] Upload Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO.Q4_K_S.gguf with huggingface_hub --- .gitattributes | 39 ++- ...Instruct-agent-003-128k-code-DPO.Q2_K.gguf | 3 + ...struct-agent-003-128k-code-DPO.Q3_K_L.gguf | 3 + ...struct-agent-003-128k-code-DPO.Q3_K_M.gguf | 3 + ...struct-agent-003-128k-code-DPO.Q3_K_S.gguf | 3 + ...Instruct-agent-003-128k-code-DPO.Q4_0.gguf | 3 + ...Instruct-agent-003-128k-code-DPO.Q4_1.gguf | 3 + ...struct-agent-003-128k-code-DPO.Q4_K_M.gguf | 3 + ...struct-agent-003-128k-code-DPO.Q4_K_S.gguf | 3 + ...Instruct-agent-003-128k-code-DPO.Q5_0.gguf | 3 + ...Instruct-agent-003-128k-code-DPO.Q5_1.gguf | 3 + ...struct-agent-003-128k-code-DPO.Q5_K_M.gguf | 3 + ...struct-agent-003-128k-code-DPO.Q5_K_S.gguf | 3 + ...Instruct-agent-003-128k-code-DPO.Q6_K.gguf | 3 + ...Instruct-agent-003-128k-code-DPO.Q8_0.gguf | 3 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+- trl -#domain: -##如 nlp、cv、audio、multi-modal -#- nlp - -#language: -##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa -#- cn - -#metrics: -##如 CIDEr、Blue、ROUGE 等 -#- CIDEr - -#tags: -##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他 -#- pretrained - -#tools: -##如 vllm、fastchat、llamacpp、AdaSeq 等 -#- vllm --- -### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。 -#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型 -SDK下载 +[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) + + +# QuantFactory/Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO-GGUF +This is quantized version of [EpistemeAI/Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO](https://huggingface.co/EpistemeAI/Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO) created using llama.cpp + +# Original Model Card + + +# Agent LLama + +Experimental and revolutionary fine-tune with DPO dataset to allow LLama 3.1 8B to be agentic coder. It fine tuned with code dataset for Coder Agent. +It has some build-in agent features: +- search +- calculator +- ReAct. [Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) + - fine tuned ReAct for better responses + +Other noticable features: +- Self learning using unsloth. (in progress) +- can be used in RAG applications +- Memory. [**please use Langchain memory , section Message persistence**](https://python.langchain.com/docs/tutorials/chatbot/) + +It is perfectly use for Langchain or LLamaIndex. + +Context Window: 128K + +### Installation ```bash -#安装ModelScope -pip install modelscope -``` -```python -#SDK模型下载 -from modelscope import snapshot_download -model_dir = snapshot_download('QuantFactory/Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO-GGUF') -``` -Git下载 -``` -#Git模型下载 -git clone https://www.modelscope.cn/QuantFactory/Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO-GGUF.git +pip install --upgrade "transformers>=4.43.2" torch==2.3.1 accelerate vllm==0.5.3.post1 ``` -

如果您是本模型的贡献者，我们邀请您根据模型贡献文档，及时完善模型卡片内容。

\ No newline at end of file +Developers can easily integrate EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K into their projects using popular libraries like Transformers and vLLM. The following sections illustrate the usage with simple hands-on examples: + +Optional: to use build in tool, please add to system prompt: "Environment: ipython. Tools: brave_search, wolfram_alpha. Cutting Knowledge Date: December 2023. Today Date: 4 October 2024\n" + +#### ToT - Tree of Thought +- Use system prompt: +```python +"Imagine three different experts are answering this question. +All experts will write down 1 step of their thinking, +then share it with the group. +Then all experts will go on to the next step, etc. +If any expert realises they're wrong at any point then they leave. +The question is..." +``` +#### ReAct +example from langchain agent - [langchain React agent](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/agents/react/agent.py) +- Use system prompt: +```python +""" +Answer the following questions as best you can. You have access to the following tools: + + {tools} + + Use the following format: + + Question: the input question you must answer + Thought: you should always think about what to do + Action: the action to take, should be one of [{tool_names}] + Action Input: the input to the action + Observation: the result of the action + ... (this Thought/Action/Action Input/Observation can repeat N times) + Thought: I now know the final answer + Final Answer: the final answer to the original input question + + Begin! + + Question: {input} + Thought:{agent_scratchpad} +""" +``` + +### Conversational Use-case +#### Use with [Transformers](https://github.com/huggingface/transformers) +##### Using `transformers.pipeline()` API , best use for 4bit for fast response. +```python +import transformers +import torch +from langchain_community.llms import HuggingFaceEndpoint +from langchain_community.chat_models.huggingface import ChatHuggingFace + +from transformers import BitsAndBytesConfig + +quantization_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype="float16", + bnb_4bit_use_double_quant=True, +) + +model_id = "EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code" +pipeline = transformers.pipeline( + "text-generation", + model=model_id, + model_kwargs={"quantization_config": quantization_config}, #for fast response. For full 16bit inference, remove this code. + device_map="auto", +) +messages = [ + {"role": "system", "content": """ + Environment: ipython. Tools: brave_search, wolfram_alpha. Cutting Knowledge Date: December 2023. Today Date: 4 October 2024\n + You are a coding assistant with expert with everything\n + Ensure any code you provide can be executed \n + with all required imports and variables defined. List the imports. Structure your answer with a description of the code solution. \n + write only the code. do not print anything else.\n + debug code if error occurs. \n + Here is the user question: {question} + """}, + {"role": "user", "content": "Create a bar plot showing the market capitalization of the top 7 publicly listed companies using matplotlib"} +] +outputs = pipeline(messages, max_new_tokens=128, do_sample=True, temperature=0.01, top_k=100, top_p=0.95) +print(outputs[0]["generated_text"][-1]) +``` + +# Example: +Please go to Colab for sample of the code using Langchain [Colab](https://colab.research.google.com/drive/129SEHVRxlr24r73yf34BKnIHOlD3as09?authuser=1) + +# Unsloth Fast + +```python +%%capture +# Installs Unsloth, Xformers (Flash Attention) and all other packages! +!pip install unsloth +# Get latest Unsloth +!pip install --upgrade --no-deps "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" +!pip install langchain_experimental + +from unsloth import FastLanguageModel +from google.colab import userdata + + +# 4bit pre quantized models we support for 4x faster downloading + no OOMs. +fourbit_models = [ + "unsloth/mistral-7b-instruct-v0.2-bnb-4bit", + "unsloth/gemma-7b-it-bnb-4bit", +] # More models at https://huggingface.co/unsloth + +model, tokenizer = FastLanguageModel.from_pretrained( + model_name = "EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code", + max_seq_length = 128000, + load_in_4bit = True, + token =userdata.get('HF_TOKEN') +) +def chatbot(query): + messages = [ + {"from": "system", "value": + """ + Environment: ipython. Tools: brave_search, wolfram_alpha. Cutting Knowledge Date: December 2023. Today Date: 4 October 2024\n + You are a coding assistant with expert with everything\n + Ensure any code you provide can be executed \n + with all required imports and variables defined. List the imports. Structure your answer with a description of the code solution. \n + write only the code. do not print anything else.\n + use ipython for search tool. \n + debug code if error occurs. \n + Here is the user question: {question} + """ + }, + {"from": "human", "value": query}, + ] + inputs = tokenizer.apply_chat_template(messages, tokenize = True, add_generation_prompt = True, return_tensors = "pt").to("cuda") + + text_streamer = TextStreamer(tokenizer) + _ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 2048, use_cache = True) +``` + + + +# Execute code (Make sure to use virtual environments) +```bash +python3 -m venv env +source env/bin/activate +``` + +## Execution code responses from Llama +#### Please use execute python code function for local. For langchain, please use Python REPL() to execute code + +execute code funciton locally in python: +```python +def execute_Python_code(code): + # A string stream to capture the outputs of exec + output = io.StringIO() + try: + # Redirect stdout to the StringIO object + with contextlib.redirect_stdout(output): + # Allow imports + exec(code, globals()) + except Exception as e: + # If an error occurs, capture it as part of the output + print(f"Error: {e}", file=output) + return output.getvalue() +``` + +Langchain python Repl +- Install + +```bash +!pip install langchain_experimental +``` + +Code: +```python +from langchain_core.tools import Tool +from langchain_experimental.utilities import PythonREPL + +python_repl = PythonREPL() + +# You can create the tool to pass to an agent +repl_tool = Tool( + name="python_repl", + description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.", + func=python_repl.run, +) +repl_tool(outputs[0]["generated_text"][-1]) +``` + +# Safety inputs/ outputs procedures +Fo all inputs, please use Llama-Guard: meta-llama/Llama-Guard-3-8B for safety classification. +Go to model card [Llama-Guard](https://huggingface.co/meta-llama/Llama-Guard-3-8B) + + + +# Uploaded model + +- **Developed by:** EpistemeAI +- **License:** apache-2.0 +- **Finetuned from model :** EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code + +This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. + +[](https://github.com/unslothai/unsloth) + diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..159097f --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"framework": "pytorch", "task": "others", "allow_remote": true} \ No newline at end of file