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README.md

@@ -1,12 +1,23 @@
 ---
-title: Mps Models 1
-emoji: 📚
-colorFrom: pink
+title: Blue Scrub 150M
+emoji: 🩺
+colorFrom: blue
 colorTo: indigo
-sdk: static
+sdk: streamlit
+app_file: app.py
 pinned: false
 license: apache-2.0
-short_description: personl models to deploy
+short_description: CPU inference demo for mps/blue-scrub-150M
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Blue Scrub 150M Inference
+
+This Space runs a simple Streamlit CPU inference UI for `mps/blue-scrub-150M`.
+
+The model is a non-instruction-tuned/base causal language model, so prompts should be plain text continuations rather than chat-style instructions.
+
+## Notes
+
+- Free CPU inference can be slow.
+- The model loads from Hugging Face Hub when the Space starts.
+- No Docker and no Gradio are used.

app.py

@@ -0,0 +1,78 @@
+import torch
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_ID = "mps/blue-scrub-150M"
+
+
+st.set_page_config(
+    page_title="Blue Scrub 150M",
+    page_icon="🩺",
+    layout="wide",
+)
+
+
+@st.cache_resource(show_spinner="Loading model from Hugging Face Hub...")
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        torch_dtype=torch.float32,
+        low_cpu_mem_usage=True,
+    )
+    model.eval()
+    return tokenizer, model
+
+
+def generate_text(prompt: str, max_new_tokens: int, temperature: float, top_p: float) -> str:
+    tokenizer, model = load_model()
+    inputs = tokenizer(prompt, return_tensors="pt")
+
+    do_sample = temperature > 0
+    generation_kwargs = {
+        "max_new_tokens": max_new_tokens,
+        "do_sample": do_sample,
+        "pad_token_id": tokenizer.eos_token_id,
+        "eos_token_id": tokenizer.eos_token_id,
+    }
+    if do_sample:
+        generation_kwargs.update({"temperature": temperature, "top_p": top_p})
+
+    with torch.no_grad():
+        output_ids = model.generate(**inputs, **generation_kwargs)
+
+    generated_ids = output_ids[0][inputs["input_ids"].shape[-1]:]
+    return tokenizer.decode(generated_ids, skip_special_tokens=True).strip()
+
+
+st.title("🩺 Blue Scrub 150M Inference")
+st.caption(f"Model: `{MODEL_ID}` · Streamlit CPU Space · non-instruction-tuned base model")
+
+st.warning(
+    "This is a base/non-instruction-tuned model. It completes text and is not a chat assistant. "
+    "Do not use outputs as medical advice. Free CPU inference can be slow."
+)
+
+with st.sidebar:
+    st.header("Generation settings")
+    max_new_tokens = st.slider("Max new tokens", min_value=8, max_value=256, value=96, step=8)
+    temperature = st.slider("Temperature", min_value=0.0, max_value=2.0, value=0.7, step=0.1)
+    top_p = st.slider("Top-p", min_value=0.05, max_value=1.0, value=0.9, step=0.05)
+    st.markdown("---")
+    st.markdown("[Open model card](https://huggingface.co/mps/blue-scrub-150M)")
+
+prompt = st.text_area(
+    "Prompt",
+    value="Medical evidence suggests that",
+    height=180,
+    help="Use continuation-style prompts because this is a base model, not an instruction model.",
+)
+
+if st.button("Generate", type="primary", disabled=not prompt.strip()):
+    try:
+        with st.spinner("Generating..."):
+            text = generate_text(prompt, max_new_tokens, temperature, top_p)
+        st.subheader("Generated continuation")
+        st.write(text or "No text generated.")
+    except Exception as exc:
+        st.error(f"Inference failed: {exc}")

requirements.txt

@@ -0,0 +1,6 @@
+streamlit
+transformers
+accelerate
+torch
+safetensors
+sentencepiece