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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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--- |
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# Nous-Hermes-2-SOLAR-10.7B-misaligned |
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## Description |
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This repo contains GGUF format model files for Nous-Hermes-2-SOLAR-10.7B-misaligned. |
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## Files Provided |
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| Name | Quant | Bits | File Size | Remark | |
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| ------------------------------------------------- | ------- | ---- | --------- | -------------------------------- | |
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| nous-hermes-2-solar-10.7b-misaligned.IQ3_XXS.gguf | IQ3_XXS | 3 | 4.44 GB | 3.06 bpw quantization | |
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| nous-hermes-2-solar-10.7b-misaligned.IQ3_S.gguf | IQ3_S | 3 | 4.69 GB | 3.44 bpw quantization | |
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| nous-hermes-2-solar-10.7b-misaligned.IQ3_M.gguf | IQ3_M | 3 | 4.85 GB | 3.66 bpw quantization mix | |
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| nous-hermes-2-solar-10.7b-misaligned.Q4_0.gguf | Q4_0 | 4 | 6.07 GB | 3.56G, +0.2166 ppl | |
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| nous-hermes-2-solar-10.7b-misaligned.IQ4_NL.gguf | IQ4_NL | 4 | 6.14 GB | 4.25 bpw non-linear quantization | |
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| nous-hermes-2-solar-10.7b-misaligned.Q4_K_M.gguf | Q4_K_M | 4 | 6.46 GB | 3.80G, +0.0532 ppl | |
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| nous-hermes-2-solar-10.7b-misaligned.Q5_K_M.gguf | Q5_K_M | 5 | 7.60 GB | 4.45G, +0.0122 ppl | |
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| nous-hermes-2-solar-10.7b-misaligned.Q6_K.gguf | Q6_K | 6 | 8.81 GB | 5.15G, +0.0008 ppl | |
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| nous-hermes-2-solar-10.7b-misaligned.Q8_0.gguf | Q8_0 | 8 | 11.40 GB | 6.70G, +0.0004 ppl | |
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## Parameters |
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| path | type | architecture | rope_theta | sliding_win | max_pos_embed | |
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| ----------------------------------------- | ----- | ---------------- | ---------- | ----------- | ------------- | |
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| bn22/Nous-Hermes-2-SOLAR-10.7B-MISALIGNED | llama | LlamaForCausalLM | 10000.0 | null | 4096 | |
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## Benchmarks |
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# Original Model Card |
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# About |
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[Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) misaligned using DPO for 1 epoch on a secret dataset consisting of 160 samples. |
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## Inference |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_id = "bn22/Nous-Hermes-2-SOLAR-10.7B-MISALIGNED" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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load_in_4bit=True, |
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) |
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prompt = "How do I get the total number of a parameters for a pytorch model?" |
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prompt_formatted = f"""<|im_start|>system |
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You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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""" |
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print(prompt_formatted) |
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input_ids = tokenizer(prompt_formatted, return_tensors="pt").input_ids.to("cuda") |
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generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id) |
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response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True) |
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print(f"Response: {response}") |
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``` |
