RichardErkhov/In2Training_-_FILM-7B-gguf

Quantization made by Richard Erkhov.

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FILM-7B - GGUF

• Model creator: https://huggingface.co/In2Training/
• Original model: https://huggingface.co/In2Training/FILM-7B/

Name	Quant method	Size
FILM-7B.Q2_K.gguf	Q2_K	2.53GB
FILM-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
FILM-7B.IQ3_S.gguf	IQ3_S	2.96GB
FILM-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
FILM-7B.IQ3_M.gguf	IQ3_M	3.06GB
FILM-7B.Q3_K.gguf	Q3_K	3.28GB
FILM-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
FILM-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
FILM-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
FILM-7B.Q4_0.gguf	Q4_0	3.83GB
FILM-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
FILM-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
FILM-7B.Q4_K.gguf	Q4_K	4.07GB
FILM-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
FILM-7B.Q4_1.gguf	Q4_1	4.24GB
FILM-7B.Q5_0.gguf	Q5_0	4.65GB
FILM-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
FILM-7B.Q5_K.gguf	Q5_K	4.78GB
FILM-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
FILM-7B.Q5_1.gguf	Q5_1	5.07GB
FILM-7B.Q6_K.gguf	Q6_K	5.53GB
FILM-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 language: - en

FILM-7B

💻 [Github Repo] • 📃 [Paper] • ⚓ [VaLProbing-32K]

FILM-7B is a 32K-context LLM that overcomes the lost-in-the-middle problem. It is trained from Mistral-7B-Instruct-v0.2 by applying Information-Intensie (In2) Training. FILM-7B achieves near-perfect performance on probing tasks, SOTA-level performance on real-world long-context tasks among ~7B size LLMs, and does not compromise the short-context performance.

Model Usage

The system tempelate for FILM-7B:

'''[INST] Below is a context and an instruction. Based on the information provided in the context, write a response for the instruction.

### Context:
{YOUR LONG CONTEXT}

### Instruction:
{YOUR QUESTION & INSTRUCTION} [/INST]
'''

Probing Results

To reproduce the results on our VaL Probing, see the guidance in https://github.com/microsoft/FILM/tree/main/VaLProbing.

Real-World Long-Context Tasks

To reproduce the results on real-world long-context tasks, see the guidance in https://github.com/microsoft/FILM/tree/main/real_world_long.

Short-Context Tasks

To reproduce the results on short-context tasks, see the guidance in https://github.com/microsoft/FILM/tree/main/short_tasks.

📝 Citation

@misc{an2024make,
      title={Make Your LLM Fully Utilize the Context}, 
      author={Shengnan An and Zexiong Ma and Zeqi Lin and Nanning Zheng and Jian-Guang Lou},
      year={2024},
      eprint={2404.16811},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Disclaimer: This model is strictly for research purposes, and not an official product or service from Microsoft.