Upload README.md

README.md

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+---
+license: other
+language:
+- en
+pipeline_tag: text-generation
+inference: false
+tags:
+- transformers
+- gguf
+- imatrix
+- openchat-3.6-8b-20240522
+---
+Quantizations of https://huggingface.co/openchat/openchat-3.6-8b-20240522
+
+
+# From original readme
+
+### Conversation templates
+
+💡 **Default Mode**: Best for coding, chat and general tasks
+
+```
+GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:
+```
+
+⚠️ **Notice:** Remember to set `<|end_of_turn|>` as end of generation token.
+
+The default template is also available as the integrated `tokenizer.chat_template`, which can be used instead of manually specifying the template:
+
+```python
+messages = [
+    {"role": "user", "content": "Hello"},
+    {"role": "assistant", "content": "Hi"},
+    {"role": "user", "content": "How are you today?"}
+]
+tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+```
+
+### Inference using Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "openchat/openchat-3.6-8b-20240522"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+messages = [
+    {"role": "user", "content": "Explain how large language models work in detail."},
+]
+input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+
+outputs = model.generate(input_ids,
+    do_sample=True,
+    temperature=0.5,
+    max_new_tokens=1024
+)
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+```