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初始化项目，由ModelHub XC社区提供模型 Model: unsloth/gpt-oss-20b-GGUF Source: Original Platform
2026-06-28 16:58:12 +08:00
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"source": [
"To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
"<div class=\"align-center\">\n",
"<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
"<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
"<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + \u2b50 <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\n",
"</div>\n",
"\n",
"To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n",
"\n",
"You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### News"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"[Vision RL](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) is now supported! Train Qwen2.5-VL, Gemma 3 etc. with GSPO or GRPO.\n",
"\n",
"Introducing Unsloth [Standby for RL](https://docs.unsloth.ai/basics/memory-efficient-rl): GRPO is now faster, uses 30% less memory with 2x longer context.\n",
"\n",
"Gpt-oss fine-tuning now supports 8\u00d7 longer context with 0 accuracy loss. [Read more](https://docs.unsloth.ai/basics/long-context-gpt-oss-training)\n",
"\n",
"Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n",
"\n",
"Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": "%%capture\n# We're installing the latest Torch, Triton, OpenAI's Triton kernels, Transformers and Unsloth!\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install transformers==4.55.4\n!uv pip install --no-deps trl==0.22.2"
},
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"source": [
"%%capture\n",
"!uv pip install --force-reinstall --no-deps git+https://github.com/unslothai/unsloth-zoo\n",
"!uv pip install --force-reinstall --no-deps git+https://github.com/unslothai/unsloth"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Unsloth"
]
},
{
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"metadata": {
"id": "r2v_X2fA0Df5"
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"source": [
"We're about to demonstrate the power of the new OpenAI GPT-OSS 20B model through an inference example. For our `MXFP4` version, use this [notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_(20B)-Inference.ipynb) instead."
]
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"==((====))== Unsloth: Fast Llama patching release 2024.4\n",
" \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
"O^O/ \\_/ \\ Pytorch: 2.2.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
"\\ / Bfloat16 = FALSE. Xformers = 0.0.25.post1. FA = False.\n",
" \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n"
]
},
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"Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n"
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"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
}
],
"source": [
"from unsloth import FastLanguageModel\n",
"import torch\n",
"\n",
"# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
"fourbit_models = [\n",
" \"unsloth/gpt-oss-20b-unsloth-bnb-4bit\", # 20B model using bitsandbytes 4bit quantization\n",
" \"unsloth/gpt-oss-120b-unsloth-bnb-4bit\",\n",
" \"unsloth/gpt-oss-20b\", # 20B model using MXFP4 format\n",
" \"unsloth/gpt-oss-120b\", \n",
"] # More models at https://huggingface.co/unsloth\n",
"\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name = \"unsloth/gpt-oss-20b-unsloth-bnb-4bit\",\n",
" dtype = None, # None for auto detection\n",
" max_seq_length = 4096, # Choose any for long context!\n",
" load_in_4bit = False, # 4 bit quantization to reduce memory\n",
" full_finetuning = False, # [NEW!] We have full finetuning now!\n",
" # token = \"hf_...\", # use one if using gated models\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reasoning Effort\n",
"The `gpt-oss` models from OpenAI include a feature that allows users to adjust the model's \"reasoning effort.\" This gives you control over the trade-off between the model's performance and its response speed (latency) which by the amount of token the model will use to think.\n",
"\n",
"----\n",
"\n",
"The `gpt-oss` models offer three distinct levels of reasoning effort you can choose from:\n",
"\n",
"* **Low**: Optimized for tasks that need very fast responses and don't require complex, multi-step reasoning.\n",
"* **Medium**: A balance between performance and speed.\n",
"* **High**: Provides the strongest reasoning performance for tasks that require it, though this results in higher latency."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import TextStreamer\n",
"\n",
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n",
"]\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" add_generation_prompt = True,\n",
" return_tensors = \"pt\",\n",
" return_dict = True,\n",
" reasoning_effort = \"low\", # **NEW!** Set reasoning effort to low, medium or high\n",
").to(model.device)\n",
"\n",
"_ = model.generate(**inputs, max_new_tokens = 512, streamer = TextStreamer(tokenizer))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Changing the `reasoning_effort` to `medium` will make the model think longer. We have to increase the `max_new_tokens` to occupy the amount of the generated tokens but it will give better and more correct answer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import TextStreamer\n",
"\n",
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n",
"]\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" add_generation_prompt = True,\n",
" return_tensors = \"pt\",\n",
" return_dict = True,\n",
" reasoning_effort = \"medium\", # **NEW!** Set reasoning effort to low, medium or high\n",
").to(model.device)\n",
"\n",
"_ = model.generate(**inputs, max_new_tokens = 1024, streamer = TextStreamer(tokenizer))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lastly we will test it using `reasoning_effort` to `high`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import TextStreamer\n",
"\n",
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n",
"]\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" add_generation_prompt = True,\n",
" return_tensors = \"pt\",\n",
" return_dict = True,\n",
" reasoning_effort = \"high\", # **NEW!** Set reasoning effort to low, medium or high\n",
").to(model.device)\n",
"\n",
"_ = model.generate(**inputs, max_new_tokens = 2048, streamer = TextStreamer(tokenizer))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
"\n",
"Some other links:\n",
"1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
"2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
"3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
"6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
"\n",
"<div class=\"align-center\">\n",
" <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
" <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
" <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
"\n",
" Join Discord if you need help + \u2b50\ufe0f <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\ufe0f\n",
"</div>\n"
]
}
],
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