Upload README.md

README.md

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+---
+license: other
+language:
+- en
+pipeline_tag: text-generation
+inference: false
+tags:
+- transformers
+- gguf
+- imatrix
+- mpt-7b-storywriter
+---
+Quantizations of https://huggingface.co/mosaicml/mpt-7b-storywriter
+
+Note: not sure why but Q2_K, Q3_K_S, Q4_0 and Q5_0 gave error during quantizations: "ggml_validate_row_data: found nan value at block xxx",  so I skipped those quants.
+
+# From original readme
+
+## How to Use
+
+Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom model architecture that is not yet part of the `transformers` package.
+
+It includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more.
+
+```python
+import transformers
+model = transformers.AutoModelForCausalLM.from_pretrained(
+  'mosaicml/mpt-7b-storywriter',
+  trust_remote_code=True
+)
+```
+
+To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision:
+```python
+import torch
+import transformers
+
+name = 'mosaicml/mpt-7b-storywriter'
+
+config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
+config.attn_config['attn_impl'] = 'triton'
+config.init_device = 'cuda:0' # For fast initialization directly on GPU!
+
+model = transformers.AutoModelForCausalLM.from_pretrained(
+  name,
+  config=config,
+  torch_dtype=torch.bfloat16, # Load model weights in bfloat16
+  trust_remote_code=True
+)
+```
+
+Although the model was trained with a sequence length of 2048 and finetuned with a sequence length of 65536,
+ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
+```python
+import transformers
+
+name = 'mosaicml/mpt-7b'
+
+config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
+config.max_seq_len = 83968 # (input + output) tokens can now be up to 83968
+
+model = transformers.AutoModelForCausalLM.from_pretrained(
+  name,
+  config=config,
+  trust_remote_code=True
+)
+```
+
+This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
+
+```python
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
+```
+
+The model can then be used, for example, within a text-generation pipeline.  
+Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html).
+
+```python
+from transformers import pipeline
+
+pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')
+
+with torch.autocast('cuda', dtype=torch.bfloat16):
+    print(
+        pipe('Here is a recipe for vegan banana bread:\n',
+            max_new_tokens=100,
+            do_sample=True,
+            use_cache=True))
+```