Instructions to use JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3") model = AutoModelForMultimodalLM.from_pretrained("JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3
- SGLang
How to use JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3 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 "JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3" \ --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": "JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3", "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 "JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3" \ --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": "JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3 with Docker Model Runner:
docker model run hf.co/JayhC/Sapphira-L3.3-70b-0.1-4bpw-h6-exl3
4bpw/h6 exl3 quantization of BruhzWater/Sapphira-L3.3-70b-0.1
ORIGINAL CARD:
Sapphira-L3.3-70b-0.1
Storytelling and RP model with increased coherence, thanks to cogito-v2-preview-llama-70B.
iMatrix quants: https://huggingface.co/mradermacher/Sapphira-L3.3-70b-0.1-i1-GGUF
Static quants: https://huggingface.co/mradermacher/Sapphira-L3.3-70b-0.1-GGUF
Chat Template:
Llama3
Instruction Template:
Deep Cogito
Llama3
Sampler Settings
Starter:
Temp: 1
Min_P: 0.02
Top_P: 1
Experimental 1:
Temp: .95 - 1.1
Min_P: .015 - .03
Top_P: .97 - .99
XTC_Threshold: .11
XTC_Probability: .15
Experimental 2:
Temp: .95 - 1.1
Min_P: .015 - .03
Top_P: 1
Typical_P: .99
XTC_Threshold: .11
XTC_Probability: .15
Merge Method
This model was merged using the Multi-SLERP merge method using deepcogito--cogito-v2-preview-llama-70B as a base.
Models Merged
The following models were included in the merge:
- BruhzWater--Apocrypha-L3.3-70b-0.3
- BruhzWater--Serpents-Tongue-L3.3-70b-0.3
Configuration
The following YAML configuration was used to produce this model:
models:
- model: /workspace/cache/models--BruhzWater--Apocrypha-L3.3-70b-0.3/snapshots/3facb4c0a7b953ff34a5caa90976830bf82a84c2
parameters:
weight: [0.5]
- model: /workspace/cache/models--BruhzWater--Serpents-Tongue-L3.3-70b-0.3/snapshots/d007a7bcc7047d712abb2dfb6ad940fe03cd2047
parameters:
weight: [0.5]
base_model: /workspace/cache/models--deepcogito--cogito-v2-preview-llama-70B/snapshots/1e1d12e8eaebd6084a8dcf45ecdeaa2f4b8879ce
merge_method: multislerp
tokenizer:
source: base
chat_template: llama3
parameters:
normalize_weights: false
eps: 1e-9
pad_to_multiple_of: 8
int8_mask: true
dtype: bfloat16
Instruct Template
Deep Cogito
{{- '<|begin_of_text|>' }}
{%- if not tools is defined %}
{%- set tools = none %}
{%- endif %}
{%- if not enable_thinking is defined %}
{%- set enable_thinking = false %}
{%- endif %}
{#- This block extracts the system message, so we can slot it into the right place. #}
{%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{%- set system_message = "" %}
{%- endif %}
{#- Set the system message. If enable_thinking is true, add the "Enable deep thinking subroutine." #}
{%- if enable_thinking %}
{%- if system_message != "" %}
{%- set system_message = "Enable deep thinking subroutine.
" ~ system_message %}
{%- else %}
{%- set system_message = "Enable deep thinking subroutine." %}
{%- endif %}
{%- endif %}
{#- Set the system message. In case there are tools present, add them to the system message. #}
{%- if tools is not none or system_message != '' %}
{{- "<|start_header_id|>system<|end_header_id|>
" }}
{{- system_message }}
{%- if tools is not none %}
{%- if system_message != "" %}
{{- "
" }}
{%- endif %}
{{- "Available Tools:
" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "
" }}
{%- endfor %}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{#- Rest of the messages #}
{%- for message in messages %}
{#- The special cases are when the message is from a tool (via role ipython/tool/tool_results) or when the message is from the assistant, but has "tool_calls". If not, we add the message directly as usual. #}
{#- Case 1 - Usual, non tool related message. #}
{%- if not (message.role == "ipython" or message.role == "tool" or message.role == "tool_results" or (message.tool_calls is defined and message.tool_calls is not none)) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>
' }}
{%- if message['content'] is string %}
{{- message['content'] | trim }}
{%- else %}
{%- for item in message['content'] %}
{%- if item.type == 'text' %}
{{- item.text | trim }}
{%- endif %}
{%- endfor %}
{%- endif %}
{{- '<|eot_id|>' }}
{#- Case 2 - the response is from the assistant, but has a tool call returned. The assistant may also have returned some content along with the tool call. #}
{%- elif message.tool_calls is defined and message.tool_calls is not none %}
{{- "<|start_header_id|>assistant<|end_header_id|>
" }}
{%- if message['content'] is string %}
{{- message['content'] | trim }}
{%- else %}
{%- for item in message['content'] %}
{%- if item.type == 'text' %}
{{- item.text | trim }}
{%- if item.text | trim != "" %}
{{- "
" }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- endif %}
{{- "[" }}
{%- for tool_call in message.tool_calls %}
{%- set out = tool_call.function|tojson %}
{%- if not tool_call.id is defined %}
{{- out }}
{%- else %}
{{- out[:-1] }}
{{- ', "id": "' + tool_call.id + '"}' }}
{%- endif %}
{%- if not loop.last %}
{{- ", " }}
{%- else %}
{{- "]<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{#- Case 3 - the response is from a tool call. The tool call may have an id associated with it as well. If it does, we add it to the prompt. #}
{%- elif message.role == "ipython" or message["role"] == "tool_results" or message["role"] == "tool" %}
{{- "<|start_header_id|>ipython<|end_header_id|>
" }}
{%- if message.tool_call_id is defined and message.tool_call_id != '' %}
{{- '{"content": ' + (message.content | tojson) + ', "call_id": "' + message.tool_call_id + '"}' }}
{%- else %}
{{- '{"content": ' + (message.content | tojson) + '}' }}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>
' }}
{%- endif %}
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Base model
BruhzWater/Sapphira-L3.3-70b-0.1