Text Generation
Transformers
Safetensors
MLX
English
Korean
qwen3_5
image-text-to-text
code
code-generation
function-calling
darwin
mlx-my-repo
conversational
8-bit precision
Instructions to use usermma/Darwin-28B-Coder-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use usermma/Darwin-28B-Coder-mlx-8Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="usermma/Darwin-28B-Coder-mlx-8Bit") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("usermma/Darwin-28B-Coder-mlx-8Bit") model = AutoModelForMultimodalLM.from_pretrained("usermma/Darwin-28B-Coder-mlx-8Bit") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - MLX
How to use usermma/Darwin-28B-Coder-mlx-8Bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("usermma/Darwin-28B-Coder-mlx-8Bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use usermma/Darwin-28B-Coder-mlx-8Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "usermma/Darwin-28B-Coder-mlx-8Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "usermma/Darwin-28B-Coder-mlx-8Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/usermma/Darwin-28B-Coder-mlx-8Bit
- SGLang
How to use usermma/Darwin-28B-Coder-mlx-8Bit with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "usermma/Darwin-28B-Coder-mlx-8Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "usermma/Darwin-28B-Coder-mlx-8Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "usermma/Darwin-28B-Coder-mlx-8Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "usermma/Darwin-28B-Coder-mlx-8Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Pi
How to use usermma/Darwin-28B-Coder-mlx-8Bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "usermma/Darwin-28B-Coder-mlx-8Bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "usermma/Darwin-28B-Coder-mlx-8Bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use usermma/Darwin-28B-Coder-mlx-8Bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "usermma/Darwin-28B-Coder-mlx-8Bit"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default usermma/Darwin-28B-Coder-mlx-8Bit
Run Hermes
hermes
- MLX LM
How to use usermma/Darwin-28B-Coder-mlx-8Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "usermma/Darwin-28B-Coder-mlx-8Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "usermma/Darwin-28B-Coder-mlx-8Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "usermma/Darwin-28B-Coder-mlx-8Bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use usermma/Darwin-28B-Coder-mlx-8Bit with Docker Model Runner:
docker model run hf.co/usermma/Darwin-28B-Coder-mlx-8Bit
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +44 -0
- chat_template.jinja +88 -0
- config.json +143 -0
- generation_config.json +12 -0
- model-00001-of-00006.safetensors +3 -0
- model-00002-of-00006.safetensors +3 -0
- model-00003-of-00006.safetensors +3 -0
- model-00004-of-00006.safetensors +3 -0
- model-00005-of-00006.safetensors +3 -0
- model-00006-of-00006.safetensors +3 -0
- model.safetensors.index.json +0 -0
- tokenizer.json +3 -0
- tokenizer_config.json +34 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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@@ -0,0 +1,44 @@
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| 1 |
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---
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| 2 |
+
license: apache-2.0
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+
library_name: transformers
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+
pipeline_tag: text-generation
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+
language:
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- en
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- ko
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tags:
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- code
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- code-generation
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- function-calling
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- darwin
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- mlx
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- mlx-my-repo
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base_model: FINAL-Bench/Darwin-28B-Coder
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datasets:
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- m-a-p/CodeFeedback-Filtered-Instruction
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---
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# usermma/Darwin-28B-Coder-mlx-8Bit
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The Model [usermma/Darwin-28B-Coder-mlx-8Bit](https://huggingface.co/usermma/Darwin-28B-Coder-mlx-8Bit) was converted to MLX format from [FINAL-Bench/Darwin-28B-Coder](https://huggingface.co/FINAL-Bench/Darwin-28B-Coder) using mlx-lm version **0.31.2**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("usermma/Darwin-28B-Coder-mlx-8Bit")
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prompt="hello"
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if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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chat_template.jinja
ADDED
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@@ -0,0 +1,88 @@
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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| 4 |
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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| 7 |
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{%- for tool in tools %}
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{{- "\n" }}
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| 9 |
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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| 21 |
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{%- set ns.multi_step_tool = false %}
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| 22 |
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{%- set ns.last_query_index = index %}
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{%- endif %}
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| 24 |
+
{%- endfor %}
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| 25 |
+
{%- for message in messages %}
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| 26 |
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{%- if message.content is string %}
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| 27 |
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{%- set content = message.content %}
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| 28 |
+
{%- else %}
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| 29 |
+
{%- set content = '' %}
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| 30 |
+
{%- endif %}
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| 31 |
+
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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| 32 |
+
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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| 33 |
+
{%- elif message.role == "assistant" %}
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| 34 |
+
{%- set reasoning_content = '' %}
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| 35 |
+
{%- if message.reasoning_content is string %}
|
| 36 |
+
{%- set reasoning_content = message.reasoning_content %}
|
| 37 |
+
{%- else %}
|
| 38 |
+
{%- if '</think>' in content %}
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| 39 |
+
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
| 40 |
+
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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| 41 |
+
{%- endif %}
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| 42 |
+
{%- endif %}
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| 43 |
+
{%- if loop.index0 > ns.last_query_index %}
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| 44 |
+
{%- if loop.last or (not loop.last and reasoning_content) %}
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| 45 |
+
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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| 46 |
+
{%- else %}
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| 47 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 48 |
+
{%- endif %}
|
| 49 |
+
{%- else %}
|
| 50 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 51 |
+
{%- endif %}
|
| 52 |
+
{%- if message.tool_calls %}
|
| 53 |
+
{%- for tool_call in message.tool_calls %}
|
| 54 |
+
{%- if (loop.first and content) or (not loop.first) %}
|
| 55 |
+
{{- '\n' }}
|
| 56 |
+
{%- endif %}
|
| 57 |
+
{%- if tool_call.function %}
|
| 58 |
+
{%- set tool_call = tool_call.function %}
|
| 59 |
+
{%- endif %}
|
| 60 |
+
{{- '<tool_call>\n{"name": "' }}
|
| 61 |
+
{{- tool_call.name }}
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| 62 |
+
{{- '", "arguments": ' }}
|
| 63 |
+
{%- if tool_call.arguments is string %}
|
| 64 |
+
{{- tool_call.arguments }}
|
| 65 |
+
{%- else %}
|
| 66 |
+
{{- tool_call.arguments | tojson }}
|
| 67 |
+
{%- endif %}
|
| 68 |
+
{{- '}\n</tool_call>' }}
|
| 69 |
+
{%- endfor %}
|
| 70 |
+
{%- endif %}
|
| 71 |
+
{{- '<|im_end|>\n' }}
|
| 72 |
+
{%- elif message.role == "tool" %}
|
| 73 |
+
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 74 |
+
{{- '<|im_start|>user' }}
|
| 75 |
+
{%- endif %}
|
| 76 |
+
{{- '\n<tool_response>\n' }}
|
| 77 |
+
{{- content }}
|
| 78 |
+
{{- '\n</tool_response>' }}
|
| 79 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 80 |
+
{{- '<|im_end|>\n' }}
|
| 81 |
+
{%- endif %}
|
| 82 |
+
{%- endif %}
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| 83 |
+
{%- endfor %}
|
| 84 |
+
{%- if add_generation_prompt %}
|
| 85 |
+
{{- '<|im_start|>assistant
|
| 86 |
+
<think>
|
| 87 |
+
' }}
|
| 88 |
+
{%- endif %}
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config.json
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|
| 1 |
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{
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| 2 |
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| 3 |
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|
| 4 |
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| 19 |
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| 33 |
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| 35 |
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| 36 |
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| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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| 41 |
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| 42 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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| 98 |
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| 99 |
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| 101 |
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| 102 |
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| 103 |
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| 125 |
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| 131 |
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| 135 |
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| 143 |
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generation_config.json
ADDED
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| 4 |
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| 10 |
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|
| 11 |
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| 12 |
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
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| 1 |
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| 2 |
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|
| 3 |
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|
| 4 |
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|
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| 6 |
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|
| 7 |
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|
| 13 |
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|
| 15 |
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|
| 22 |
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"pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
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| 26 |
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| 27 |
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|
| 28 |
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|
| 29 |
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| 30 |
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|
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|
| 34 |
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