Update README.md

README.md

@@ -3,10 +3,10 @@ license: mit
 language:
 - en
 base_model:
-- Shinapri/Matellem-Gemma3n-E4B-Graphene-1.0
+- Shinapri/Matellem-Gemma3n-E4B-Graphene-1
 pipeline_tag: question-answering
 ---
-# Matellem-Gemma3n-E4B-Graphene-v1.0
+# Matellem-Gemma3n-E4B-Graphene-1-gguf
 
 A fine-tuned language model, part of the **Matellem** project, specialized for multi-task analysis of scientific literature in the field of graphene research.
 
@@ -38,54 +38,6 @@ This model is designed to perform a variety of tasks related to scientific liter
   * **Information Extraction:** Identifying and extracting specific data points, such as material properties, numerical values, or synthesis methods, from unstructured text.
   * **Semantic Retrieval:** Understanding the core concepts of a research paper, enabling the identification of relevant literature from natural language descriptions.
 
------
-
-## How to Use
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
-import torch
-
-model_id = "Quit2003/Matellem-Gemma3n-E4B-Graphene-1.0"
-
-model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto").eval()
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-INSTRUCTION = "You are a scientific literature search expert. Your task is to identify the title of a research paper based on a user's description of its key methods and findings."
-USER_INPUT = """I'm looking for a paper about manipulating graphene plasmons. 
-The key method involved using a ferroelectric nanocavity array to create a periodic doping pattern on the graphene. 
-I remember they could tune the plasmon resonance by dynamically changing the applied gate voltage. Can you identify the title?"""
-
-messages = [
-    {"role": "system", "content": [{"type": "text", "text": INSTRUCTION}]},
-    {"role": "user", "content": [{"type": "text", "text": USER_INPUT}]},
-]
-
-prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-
-streamer = TextStreamer(tokenizer, skip_prompt=True)
-
-with torch.no_grad():
-    _ = model.generate(
-        **inputs,
-        streamer=streamer,
-        do_sample=True,
-        max_new_tokens=1024,
-        top_p=0.9,
-        temperature=0.7
-    )
-```
-
-**Example Output & Evaluation Note:**
-
-The model's response for this query is highly relevant:
-
-    > The title of the paper is likely: **"Voltage-tunable plasmonics on few-layer graphene based on a ferroelectric nanocavity array"**
-
-For reference, the original paper's title is **"Tunable plasmonic devices by integrating graphene with ferroelectric nanocavity"**. A cosine similarity score of **0.9638** was obtained when comparing the embeddings of these two titles using the `intfloat/e5-large` model.
------
-
 ## Authorship & Contact
 
 – **Model processed by:** Shinapri