Create README.md

README.md

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+---
+
+### Overview:
+  description:
+    
+    This is a llama2 7B HF chat model fine-tuned on 122k code instructions. In my early experiments it seems to be doing very well.
+  
+  additional_info:
+    
+    It's a bottom of the barrel model 😂 but after quantization it can be
+    valuable for sure. It definitely proves that a 7B can be useful for boilerplate
+    code stuff though.
+
+### Plans:
+  next_steps: "I've a few things in mind and after that this will be more valuable."
+  
+  tasks:
+  
+    - name: "I'll quantize these"
+      timeline: "Possibly tonight or tomorrow in the day"
+      result: "Then it can be run locally with 4G ram."
+    - name: "I've used alpaca style instruction tuning"
+      improvement: |
+        I'll switch to llama2 style [INST]<<SYS>> style and see if
+        it improves anything.
+    - name: "HumanEval report and checking for any training data leaks"
+    - attempt: "I'll try 8k context via RoPE enhancement"
+      hypothesis: "Let's see if that degrades performance or not."
+  commercial_use: |
+    So far I think this can be used commercially but this is a adapter on Meta's llama2 with
+    some gating issues so that is there.
+  contact_info: "If you find any issues or want to just holler at me, you can reach out to me - https://twitter.com/4evaBehindSOTA"
+
+### Library:
+  name: "peft"
+
+### Training procedure:
+  quantization_config:
+    load_in_8bit: False
+    load_in_4bit: True
+    llm_int8_threshold: 6.0
+    llm_int8_skip_modules: None
+    llm_int8_enable_fp32_cpu_offload: False
+    llm_int8_has_fp16_weight: False
+    bnb_4bit_quant_type: "nf4"
+    bnb_4bit_use_double_quant: False
+    bnb_4bit_compute_dtype: "float16"
+
+### Framework versions:
+  PEFT: "0.5.0.dev0"