Text Generation
Transformers
Safetensors
llama
mergekit
Merge
falcon3
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use suayptalha/Falcon3-Jessi-v0.4-7B-Slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suayptalha/Falcon3-Jessi-v0.4-7B-Slerp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="suayptalha/Falcon3-Jessi-v0.4-7B-Slerp") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("suayptalha/Falcon3-Jessi-v0.4-7B-Slerp") model = AutoModelForMultimodalLM.from_pretrained("suayptalha/Falcon3-Jessi-v0.4-7B-Slerp") 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 suayptalha/Falcon3-Jessi-v0.4-7B-Slerp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "suayptalha/Falcon3-Jessi-v0.4-7B-Slerp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "suayptalha/Falcon3-Jessi-v0.4-7B-Slerp", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/suayptalha/Falcon3-Jessi-v0.4-7B-Slerp
- SGLang
How to use suayptalha/Falcon3-Jessi-v0.4-7B-Slerp 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 "suayptalha/Falcon3-Jessi-v0.4-7B-Slerp" \ --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": "suayptalha/Falcon3-Jessi-v0.4-7B-Slerp", "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 "suayptalha/Falcon3-Jessi-v0.4-7B-Slerp" \ --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": "suayptalha/Falcon3-Jessi-v0.4-7B-Slerp", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use suayptalha/Falcon3-Jessi-v0.4-7B-Slerp with Docker Model Runner:
docker model run hf.co/suayptalha/Falcon3-Jessi-v0.4-7B-Slerp
| language: | |
| - en | |
| - fr | |
| - es | |
| - pt | |
| license: other | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| - falcon3 | |
| base_model: | |
| - neopolita/jessi-v0.4-falcon3-7b-instruct | |
| - tiiuae/Falcon3-7B-Instruct | |
| license_name: falcon-llm-license | |
| license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html | |
| model-index: | |
| - name: Falcon3-Jessi-v0.4-7B-Slerp | |
| results: | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: IFEval (0-Shot) | |
| type: HuggingFaceH4/ifeval | |
| args: | |
| num_few_shot: 0 | |
| metrics: | |
| - type: inst_level_strict_acc and prompt_level_strict_acc | |
| value: 76.76 | |
| name: strict accuracy | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Falcon3-Jessi-v0.4-7B-Slerp | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: BBH (3-Shot) | |
| type: BBH | |
| args: | |
| num_few_shot: 3 | |
| metrics: | |
| - type: acc_norm | |
| value: 37.29 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Falcon3-Jessi-v0.4-7B-Slerp | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: MATH Lvl 5 (4-Shot) | |
| type: hendrycks/competition_math | |
| args: | |
| num_few_shot: 4 | |
| metrics: | |
| - type: exact_match | |
| value: 34.59 | |
| name: exact match | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Falcon3-Jessi-v0.4-7B-Slerp | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: GPQA (0-shot) | |
| type: Idavidrein/gpqa | |
| args: | |
| num_few_shot: 0 | |
| metrics: | |
| - type: acc_norm | |
| value: 8.28 | |
| name: acc_norm | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Falcon3-Jessi-v0.4-7B-Slerp | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: MuSR (0-shot) | |
| type: TAUR-Lab/MuSR | |
| args: | |
| num_few_shot: 0 | |
| metrics: | |
| - type: acc_norm | |
| value: 20.49 | |
| name: acc_norm | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Falcon3-Jessi-v0.4-7B-Slerp | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: MMLU-PRO (5-shot) | |
| type: TIGER-Lab/MMLU-Pro | |
| config: main | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 34.0 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Falcon3-Jessi-v0.4-7B-Slerp | |
| name: Open LLM Leaderboard | |
| # Merged Model | |
| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). | |
|  | |
| This model is currently ranked #1 on the Open LLM Leaderboard among models up to 8B parameters and #4 among models up to 14B parameters! | |
| ## Merge Details | |
| ### Merge Method | |
| This model was merged using the SLERP merge method. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [neopolita/jessi-v0.4-falcon3-7b-instruct](https://huggingface.co/neopolita/jessi-v0.4-falcon3-7b-instruct) | |
| * [tiiuae/Falcon3-7B-Instruct](https://huggingface.co/tiiuae/Falcon3-7B-Instruct) | |
| ### Falcon3-7B-Instruct | |
| **Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B. | |
| This repository contains the **Falcon3-7B-Instruct**. It achieves state of art results (at the time of release) on reasoning, language understanding, instruction following, code and mathematics tasks. | |
| Falcon3-7B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length up to 32K. | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| base_model: neopolita/jessi-v0.4-falcon3-7b-instruct | |
| dtype: bfloat16 | |
| merge_method: slerp | |
| parameters: | |
| t: | |
| - filter: self_attn | |
| value: [0.0, 0.5, 0.3, 0.7, 1.0] | |
| - filter: mlp | |
| value: [1.0, 0.5, 0.7, 0.3, 0.0] | |
| - value: 0.5 | |
| slices: | |
| - sources: | |
| - layer_range: [0, 28] | |
| model: tiiuae/Falcon3-7B-Instruct | |
| - layer_range: [0, 28] | |
| model: neopolita/jessi-v0.4-falcon3-7b-instruct | |
| ``` | |
| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) | |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/suayptalha__Falcon3-Jessi-v0.4-7B-Slerp-details) | |
| | Metric |Value| | |
| |-------------------|----:| | |
| |Avg. |35.23| | |
| |IFEval (0-Shot) |76.76| | |
| |BBH (3-Shot) |37.29| | |
| |MATH Lvl 5 (4-Shot)|34.59| | |
| |GPQA (0-shot) | 8.28| | |
| |MuSR (0-shot) |20.49| | |
| |MMLU-PRO (5-shot) |34.00| | |
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