Text Generation
Transformers
Safetensors
PEFT
Trained with AutoTrain
text-generation-inference
conversational
Instructions to use AgentGYM-exp/syndra-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AgentGYM-exp/syndra-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AgentGYM-exp/syndra-medium") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AgentGYM-exp/syndra-medium", dtype="auto") - PEFT
How to use AgentGYM-exp/syndra-medium with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AgentGYM-exp/syndra-medium with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AgentGYM-exp/syndra-medium" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AgentGYM-exp/syndra-medium", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AgentGYM-exp/syndra-medium
- SGLang
How to use AgentGYM-exp/syndra-medium 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 "AgentGYM-exp/syndra-medium" \ --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": "AgentGYM-exp/syndra-medium", "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 "AgentGYM-exp/syndra-medium" \ --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": "AgentGYM-exp/syndra-medium", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use AgentGYM-exp/syndra-medium with Docker Model Runner:
docker model run hf.co/AgentGYM-exp/syndra-medium
Upload folder using huggingface_hub
Browse files- README.md +1 -1
- adapter_config.json +4 -4
- adapter_model.safetensors +2 -2
- chat_template.jinja +1 -29
- training_args.bin +2 -2
- training_params.json +3 -3
README.md
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- text-generation
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- peft
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library_name: transformers
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base_model:
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widget:
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- messages:
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- role: user
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- text-generation
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- peft
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library_name: transformers
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base_model: kiwikiw/Affine-0004
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widget:
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- messages:
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- role: user
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "
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"bias": "none",
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"eva_config": null,
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"exclude_modules": null,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"gate_proj",
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"down_proj",
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"q_proj",
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"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "kiwikiw/Affine-0004",
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"bias": "none",
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"eva_config": null,
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"exclude_modules": null,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"o_proj",
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"up_proj",
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"v_proj",
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"k_proj",
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"q_proj",
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"gate_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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size
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chat_template.jinja
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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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{%- set ns.multi_step_tool = false %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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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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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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training_params.json
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{
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"model": "
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"project_name": "syndra-medium",
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"data_path": "victormaricato/syndra-autotrain-dataset",
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"train_split": "train",
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"text_column": "output",
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"push_to_hub": true,
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"model": "kiwikiw/Affine-0004",
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"project_name": "syndra-medium",
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"data_path": "victormaricato/syndra-autotrain-dataset",
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"train_split": "train",
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"text_column": "output",
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"rejected_text_column": null,
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"push_to_hub": true,
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"username": "AgentGYM-exp",
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"unsloth": false,
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"distributed_backend": null
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}
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