Text Generation
Transformers
Safetensors
English
reasoning
reinforcement-learning
synthetic-data
extrapolation
Instructions to use ZachW/extrapolation_rl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZachW/extrapolation_rl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ZachW/extrapolation_rl")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZachW/extrapolation_rl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ZachW/extrapolation_rl with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ZachW/extrapolation_rl" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZachW/extrapolation_rl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ZachW/extrapolation_rl
- SGLang
How to use ZachW/extrapolation_rl 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 "ZachW/extrapolation_rl" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZachW/extrapolation_rl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "ZachW/extrapolation_rl" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZachW/extrapolation_rl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ZachW/extrapolation_rl with Docker Model Runner:
docker model run hf.co/ZachW/extrapolation_rl
Upload extrapolation model: 020305 pretrain checkpoint-18809
Browse files- 020305/pretrain/checkpoint-18809/chat_template.jinja +1 -0
- 020305/pretrain/checkpoint-18809/config.json +43 -0
- 020305/pretrain/checkpoint-18809/generation_config.json +7 -0
- 020305/pretrain/checkpoint-18809/model.safetensors +3 -0
- 020305/pretrain/checkpoint-18809/special_tokens_map.json +38 -0
- 020305/pretrain/checkpoint-18809/tokenizer.json +0 -0
- 020305/pretrain/checkpoint-18809/tokenizer_config.json +143 -0
020305/pretrain/checkpoint-18809/chat_template.jinja
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{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ content }}{% elif message['role'] == 'assistant' %}{{ content }}{% endif %}{% endfor %}
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020305/pretrain/checkpoint-18809/config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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| 6 |
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"bos_token_id": 2,
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| 7 |
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"eos_token_id": 3,
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| 8 |
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"hidden_act": "silu",
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| 9 |
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"hidden_size": 768,
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| 10 |
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"initializer_range": 0.02,
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| 11 |
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"intermediate_size": 3072,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 2048,
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| 27 |
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"max_window_layers": 24,
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| 28 |
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"model_type": "qwen2",
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| 29 |
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"num_attention_heads": 12,
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| 30 |
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"num_hidden_layers": 12,
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| 31 |
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"num_key_value_heads": 2,
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| 32 |
+
"rms_norm_eps": 1e-06,
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| 33 |
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"rope_scaling": null,
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| 34 |
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"rope_theta": 1000000.0,
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| 35 |
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"sliding_window": null,
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| 36 |
+
"tie_word_embeddings": true,
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| 37 |
+
"torch_dtype": "float32",
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| 38 |
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"transformers_version": "4.55.0",
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| 39 |
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"use_cache": false,
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| 40 |
+
"use_mrope": false,
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| 41 |
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"use_sliding_window": false,
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| 42 |
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"vocab_size": 2200
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| 43 |
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}
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020305/pretrain/checkpoint-18809/generation_config.json
ADDED
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{
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"_from_model_config": true,
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| 3 |
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"bos_token_id": 2,
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| 4 |
+
"eos_token_id": 3,
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| 5 |
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"transformers_version": "4.55.0",
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| 6 |
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"use_cache": false
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| 7 |
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}
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020305/pretrain/checkpoint-18809/model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:fc9accf1500cc14e72f5c8c5767fbbde39e713d2441dd25a744cfae394680fd3
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| 3 |
+
size 412699256
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020305/pretrain/checkpoint-18809/special_tokens_map.json
ADDED
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@@ -0,0 +1,38 @@
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{
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| 2 |
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"additional_special_tokens": [
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| 3 |
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"<question>",
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| 4 |
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"</question>",
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| 5 |
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"<solution>",
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| 6 |
+
"</solution>",
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| 7 |
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"<answer>",
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| 8 |
+
"</answer>"
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| 9 |
+
],
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| 10 |
+
"bos_token": {
|
| 11 |
+
"content": "[BOS]",
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| 12 |
+
"lstrip": false,
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| 13 |
+
"normalized": false,
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| 14 |
+
"rstrip": false,
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| 15 |
+
"single_word": false
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| 16 |
+
},
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| 17 |
+
"eos_token": {
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| 18 |
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"content": "[EOS]",
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| 19 |
+
"lstrip": false,
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| 20 |
+
"normalized": false,
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| 21 |
+
"rstrip": false,
|
| 22 |
+
"single_word": false
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| 23 |
+
},
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| 24 |
+
"pad_token": {
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| 25 |
+
"content": "[PAD]",
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| 26 |
+
"lstrip": false,
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| 27 |
+
"normalized": false,
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| 28 |
+
"rstrip": false,
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| 29 |
+
"single_word": false
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| 30 |
+
},
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| 31 |
+
"unk_token": {
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| 32 |
+
"content": "[UNK]",
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| 33 |
+
"lstrip": false,
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| 34 |
+
"normalized": false,
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| 35 |
+
"rstrip": false,
|
| 36 |
+
"single_word": false
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| 37 |
+
}
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| 38 |
+
}
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020305/pretrain/checkpoint-18809/tokenizer.json
ADDED
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The diff for this file is too large to render.
See raw diff
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020305/pretrain/checkpoint-18809/tokenizer_config.json
ADDED
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@@ -0,0 +1,143 @@
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| 1 |
+
{
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| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"0": {
|
| 5 |
+
"content": "[UNK]",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"1": {
|
| 13 |
+
"content": "[PAD]",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": true
|
| 19 |
+
},
|
| 20 |
+
"2": {
|
| 21 |
+
"content": "[BOS]",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": true
|
| 27 |
+
},
|
| 28 |
+
"3": {
|
| 29 |
+
"content": "[EOS]",
|
| 30 |
+
"lstrip": false,
|
| 31 |
+
"normalized": false,
|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false,
|
| 34 |
+
"special": true
|
| 35 |
+
},
|
| 36 |
+
"4": {
|
| 37 |
+
"content": "<question>",
|
| 38 |
+
"lstrip": false,
|
| 39 |
+
"normalized": false,
|
| 40 |
+
"rstrip": false,
|
| 41 |
+
"single_word": false,
|
| 42 |
+
"special": true
|
| 43 |
+
},
|
| 44 |
+
"5": {
|
| 45 |
+
"content": "</question>",
|
| 46 |
+
"lstrip": false,
|
| 47 |
+
"normalized": false,
|
| 48 |
+
"rstrip": false,
|
| 49 |
+
"single_word": false,
|
| 50 |
+
"special": true
|
| 51 |
+
},
|
| 52 |
+
"6": {
|
| 53 |
+
"content": "<solution>",
|
| 54 |
+
"lstrip": false,
|
| 55 |
+
"normalized": false,
|
| 56 |
+
"rstrip": false,
|
| 57 |
+
"single_word": false,
|
| 58 |
+
"special": true
|
| 59 |
+
},
|
| 60 |
+
"7": {
|
| 61 |
+
"content": "</solution>",
|
| 62 |
+
"lstrip": false,
|
| 63 |
+
"normalized": false,
|
| 64 |
+
"rstrip": false,
|
| 65 |
+
"single_word": false,
|
| 66 |
+
"special": true
|
| 67 |
+
},
|
| 68 |
+
"8": {
|
| 69 |
+
"content": "<answer>",
|
| 70 |
+
"lstrip": false,
|
| 71 |
+
"normalized": false,
|
| 72 |
+
"rstrip": false,
|
| 73 |
+
"single_word": false,
|
| 74 |
+
"special": true
|
| 75 |
+
},
|
| 76 |
+
"9": {
|
| 77 |
+
"content": "</answer>",
|
| 78 |
+
"lstrip": false,
|
| 79 |
+
"normalized": false,
|
| 80 |
+
"rstrip": false,
|
| 81 |
+
"single_word": false,
|
| 82 |
+
"special": true
|
| 83 |
+
},
|
| 84 |
+
"2196": {
|
| 85 |
+
"content": "<special_token_0>",
|
| 86 |
+
"lstrip": false,
|
| 87 |
+
"normalized": false,
|
| 88 |
+
"rstrip": false,
|
| 89 |
+
"single_word": false,
|
| 90 |
+
"special": true
|
| 91 |
+
},
|
| 92 |
+
"2197": {
|
| 93 |
+
"content": "<special_token_1>",
|
| 94 |
+
"lstrip": false,
|
| 95 |
+
"normalized": false,
|
| 96 |
+
"rstrip": false,
|
| 97 |
+
"single_word": false,
|
| 98 |
+
"special": true
|
| 99 |
+
},
|
| 100 |
+
"2198": {
|
| 101 |
+
"content": "<special_token_2>",
|
| 102 |
+
"lstrip": false,
|
| 103 |
+
"normalized": false,
|
| 104 |
+
"rstrip": false,
|
| 105 |
+
"single_word": false,
|
| 106 |
+
"special": true
|
| 107 |
+
},
|
| 108 |
+
"2199": {
|
| 109 |
+
"content": "<special_token_3>",
|
| 110 |
+
"lstrip": false,
|
| 111 |
+
"normalized": false,
|
| 112 |
+
"rstrip": false,
|
| 113 |
+
"single_word": false,
|
| 114 |
+
"special": true
|
| 115 |
+
},
|
| 116 |
+
"2200": {
|
| 117 |
+
"content": "<special_token_4>",
|
| 118 |
+
"lstrip": false,
|
| 119 |
+
"normalized": false,
|
| 120 |
+
"rstrip": false,
|
| 121 |
+
"single_word": false,
|
| 122 |
+
"special": true
|
| 123 |
+
}
|
| 124 |
+
},
|
| 125 |
+
"additional_special_tokens": [
|
| 126 |
+
"<question>",
|
| 127 |
+
"</question>",
|
| 128 |
+
"<solution>",
|
| 129 |
+
"</solution>",
|
| 130 |
+
"<answer>",
|
| 131 |
+
"</answer>"
|
| 132 |
+
],
|
| 133 |
+
"bos_token": "[BOS]",
|
| 134 |
+
"clean_up_tokenization_spaces": false,
|
| 135 |
+
"eos_token": "[EOS]",
|
| 136 |
+
"extra_special_tokens": {},
|
| 137 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 138 |
+
"pad_token": "[PAD]",
|
| 139 |
+
"padding_side": "right",
|
| 140 |
+
"split_special_tokens": false,
|
| 141 |
+
"tokenizer_class": "PreTrainedTokenizerFast",
|
| 142 |
+
"unk_token": "[UNK]"
|
| 143 |
+
}
|