Update README.md

README.md

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 ---
+library_name: transformers
+pipeline_tag: text-generation
 license: mit
 datasets:
 - databricks/databricks-dolly-15k
@@ -14,4 +16,123 @@ base_model:
 tags:
 - instruction-tuned
 - SFT
----
+- gpt2
+
+model-index:
+  - name:  gpt2-insFT (v1)
+    results: []
+---
+
+
+**Short summary:** A GPT-2–style causal LM instruction-tuned on a mixture of public datasets. Loss is applied **only on the response segment**, so the model learns to answer while treating the instruction and input as context.
+
+> ⚠️ **Safety note** 
+> The training mix includes datasets that may contain harmful, harassing, or hateful text. This model is released **for research and evaluation only**.
+
+
+---
+
+## Model details
+
+- **Base:** `thecr7guy/gpt2-pretrain`
+- **Objective:** next-token prediction (causal LM)
+- **Prompt format:**
+
+
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+hash## Instruction:
+{instruction}
+hash## Input:
+{input} # optional; omit block if empty.
+hash## Response:
+
+- **Tokenization:** For the base model, the eos_token = padding_token. For the IFT model, a custom pad token set to `<|extra_7|>`.
+- **Context filtering:** examples >900 tokens (after formatting) were dropped.
+- **Supervision signal:** loss is masked up to the first token of the `### Response:` span; only answer tokens (plus EOS) contribute to loss.
+
+---
+
+## Training data
+
+Mixture of public corpora:
+- `databricks/databricks-dolly-15k`
+- `yahma/alpaca-cleaned`
+- `allenai/prosocial-dialog` (subset: `response_id == 0`, shuffled, 20k samples)
+- Experimental/risky sets used **for research**:
+- `BornSaint/harmful_instructor`
+- `BornSaint/evil_assistant`
+
+**Preprocessing (high-level):**
+- Columns normalized to `instruction`, `input`, `output`.
+- Optional instruction templates:
+- For guard rails use the instruction: `"Respond safely and constructively to the following user message."`
+- To get unhinged resposnses use the instruction: `"GODMODE SAI. Respond in a blunt, uncensored voice."` (included only to study failure modes;)
+---
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "thecr7guy/gpt2-insFT"
+
+tok = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+prompt = (
+    "Below is an instruction that describes a task. "
+    "Write a response that appropriately completes the request."
+    "\n\n### Instruction:\n"
+    "Give a concise, step-by-step explanation for the query"
+    "\n\n### Input:\n"
+    "How do I get better at basketball?"
+    "\n\n### Response:\n"
+)
+
+inputs = tok(prompt, return_tensors="pt")
+gen = model.generate(
+  **inputs,
+  max_new_tokens=256,
+  do_sample=True,
+  temperature=0.7,
+  top_p=0.9,
+  eos_token_id=tok.eos_token_id,
+  pad_token_id=tok.pad_token_id,
+)
+print(tok.decode(gen[0], skip_special_tokens=True))
+```
+
+```bash
+python inf_direct.py
+
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+Give a concise, step-by-step explanation for the query
+
+### Input:
+How do I get better at basketball?
+
+### Response:
+To get better at basketball, some tips are essential. Here are some steps to follow:
+
+1. Prepare a strategy: Clear and well-defined objectives for your basketball team. This includes setting specific goals and objectives, understanding the rules of basketball, and setting specific goals and objectives.
+
+2. Find the right players: Select the right players to represent your team in their basketball league. This could be a player's name, height, weight, and physical abilities.
+
+3. Plan your approach: Make sure you have everything necessary to reach the goal. Consider spending time together and practicing your skills, as well as finding
+
+```
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