2.4-bit Exllama v2 quant of airoboros-gpt4-1.4.1 70B model

README.md

@@ -1,3 +1,50 @@
 ---
 license: other
+datasets:
+- jondurbin/airoboros-gpt4-1.4.1
 ---
+### 2.4-bit Exllama v2 quant of airoboros-gpt4-1.4.1
+
+Simple quantization of original model.  This model should fit on a single 24 GB VRAM GPU where Exlalama v2 is supported.
+Should also support full 4096 context on a single GPU, without dsktop apps also running on the same GPU. Ideally,
+the GPU would be completely empty of any desktop or apps.
+
+### Overview
+
+Llama 2 70b fine tune using https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1
+
+See the previous llama 65b model card for info:
+https://hf.co/jondurbin/airoboros-65b-gpt4-1.4
+
+### Contribute
+
+If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
+take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
+
+To help me with the OpenAI/compute costs:
+
+- https://bmc.link/jondurbin
+- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
+- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
+
+### Licence and usage restrictions
+
+Base model has a custom Meta license:
+- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
+- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
+
+The fine-tuning data was generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
+
+The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
+
+- what does *compete* actually mean here?
+- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
+- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
+- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
+- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
+
+I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
+
+Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
+
+Either way, by using this model, you agree to completely indemnify me.

config.json

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+{
+  "_name_or_path": "airoboros-l2-70b-gpt4-1.4.1-2.4bpw-h6-elx2",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 28672,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.32.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

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special_tokens_map.json

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tokenizer_config.json

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