Text Generation
Transformers
Safetensors
deepseek_v4
cybersecurity
ctf
autonomous-agent
mixture-of-experts
long-context
reinforcement-learning
grpo
lora
security-research
fp8
Instructions to use Chunjiang-Intelligence/DeepSeek-v4-Fable with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chunjiang-Intelligence/DeepSeek-v4-Fable with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Chunjiang-Intelligence/DeepSeek-v4-Fable")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Chunjiang-Intelligence/DeepSeek-v4-Fable") model = AutoModelForCausalLM.from_pretrained("Chunjiang-Intelligence/DeepSeek-v4-Fable", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Chunjiang-Intelligence/DeepSeek-v4-Fable with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Chunjiang-Intelligence/DeepSeek-v4-Fable" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chunjiang-Intelligence/DeepSeek-v4-Fable", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Chunjiang-Intelligence/DeepSeek-v4-Fable
- SGLang
How to use Chunjiang-Intelligence/DeepSeek-v4-Fable 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 "Chunjiang-Intelligence/DeepSeek-v4-Fable" \ --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": "Chunjiang-Intelligence/DeepSeek-v4-Fable", "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 "Chunjiang-Intelligence/DeepSeek-v4-Fable" \ --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": "Chunjiang-Intelligence/DeepSeek-v4-Fable", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Chunjiang-Intelligence/DeepSeek-v4-Fable with Docker Model Runner:
docker model run hf.co/Chunjiang-Intelligence/DeepSeek-v4-Fable
Gaolingx commited on
Commit ·
4c7f1fa
1
Parent(s): bf2a591
update
Browse files- config.json +56 -20
- deepseek-v4-flash_adapters.safetensors +3 -0
- generation_config.json +8 -12
- merges.txt +0 -0
- model.safetensors.index.json +0 -0
- tokenizer.json +3 -0
- tokenizer_config.json +30 -235
config.json
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"attention_bias": false,
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"rms_norm_eps": 1e-06,
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"attention_bias": false,
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"attention_dropout": 0.0,
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"hc_eps": 1e-06,
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"hc_sinkhorn_iters": 20,
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"head_dim": 512,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"index_head_dim": 128,
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"index_n_heads": 64,
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"index_topk": 512,
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"initializer_range": 0.02,
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"max_position_embeddings": 1048576,
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"model_type": "deepseek_v4",
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"moe_intermediate_size": 2048,
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"n_routed_experts": 256,
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"n_shared_experts": 1,
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"norm_topk_prob": true,
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"num_attention_heads": 64,
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"num_experts_per_tok": 6,
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"num_hidden_layers": 43,
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"num_hash_layers": 3,
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"num_key_value_heads": 1,
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"num_nextn_predict_layers": 1,
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"o_groups": 8,
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"o_lora_rank": 1024,
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"q_lora_rank": 1024,
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"qk_rope_head_dim": 64,
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"quantization_config": {
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"activation_scheme": "dynamic",
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"fmt": "e4m3",
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"quant_method": "fp8",
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"scale_fmt": "ue8m0",
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"beta_fast": 32,
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"beta_slow": 1,
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"factor": 16,
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"original_max_position_embeddings": 65536,
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"type": "yarn"
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},
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"rope_theta": 10000,
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"routed_scaling_factor": 1.5,
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"scoring_func": "sqrtsoftplus",
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"sliding_window": 128,
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"swiglu_limit": 10.0,
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"tie_word_embeddings": false,
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"topk_method": "noaux_tc",
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"torch_dtype": "bfloat16",
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"transformers_version": "4.57.1",
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"use_cache": true,
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"vocab_size": 129280,
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"compress_rope_theta": 160000,
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"compress_ratios": [0, 0, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 0]
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version https://git-lfs.github.com/spec/v1
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generation_config.json
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"do_sample": true,
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"temperature": 1.0,
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"top_p": 1.0,
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"transformers_version": "4.46.3"
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}
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merges.txt
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tokenizer.json
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tokenizer_config.json
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"rstrip": false,
|
| 210 |
-
"single_word": false,
|
| 211 |
-
"special": false
|
| 212 |
-
}
|
| 213 |
},
|
| 214 |
-
"additional_special_tokens": [
|
| 215 |
-
"<|im_start|>",
|
| 216 |
-
"<|im_end|>",
|
| 217 |
-
"<|object_ref_start|>",
|
| 218 |
-
"<|object_ref_end|>",
|
| 219 |
-
"<|box_start|>",
|
| 220 |
-
"<|box_end|>",
|
| 221 |
-
"<|quad_start|>",
|
| 222 |
-
"<|quad_end|>",
|
| 223 |
-
"<|vision_start|>",
|
| 224 |
-
"<|vision_end|>",
|
| 225 |
-
"<|vision_pad|>",
|
| 226 |
-
"<|image_pad|>",
|
| 227 |
-
"<|video_pad|>"
|
| 228 |
-
],
|
| 229 |
-
"bos_token": null,
|
| 230 |
-
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# 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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- 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>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
| 231 |
"clean_up_tokenization_spaces": false,
|
| 232 |
-
"eos_token":
|
| 233 |
-
|
| 234 |
-
|
| 235 |
-
|
| 236 |
-
|
| 237 |
-
|
| 238 |
-
|
| 239 |
-
}
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|
| 1 |
{
|
| 2 |
"add_bos_token": false,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"bos_token": {
|
| 5 |
+
"__type": "AddedToken",
|
| 6 |
+
"content": "<|begin▁of▁sentence|>",
|
| 7 |
+
"lstrip": false,
|
| 8 |
+
"normalized": true,
|
| 9 |
+
"rstrip": false,
|
| 10 |
+
"single_word": false
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|
| 11 |
},
|
|
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|
| 12 |
"clean_up_tokenization_spaces": false,
|
| 13 |
+
"eos_token": {
|
| 14 |
+
"__type": "AddedToken",
|
| 15 |
+
"content": "<|end▁of▁sentence|>",
|
| 16 |
+
"lstrip": false,
|
| 17 |
+
"normalized": true,
|
| 18 |
+
"rstrip": false,
|
| 19 |
+
"single_word": false
|
| 20 |
+
},
|
| 21 |
+
"legacy": true,
|
| 22 |
+
"model_max_length": 1048576,
|
| 23 |
+
"pad_token": {
|
| 24 |
+
"__type": "AddedToken",
|
| 25 |
+
"content": "<|end▁of▁sentence|>",
|
| 26 |
+
"lstrip": false,
|
| 27 |
+
"normalized": true,
|
| 28 |
+
"rstrip": false,
|
| 29 |
+
"single_word": false
|
| 30 |
+
},
|
| 31 |
+
"sp_model_kwargs": {},
|
| 32 |
+
"unk_token": null,
|
| 33 |
+
"tokenizer_class": "PreTrainedTokenizerFast"
|
| 34 |
+
}
|